Overview

Dataset statistics

Number of variables79
Number of observations50
Missing cells1389
Missing cells (%)35.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.0 KiB
Average record size in memory634.6 B

Variable types

Numeric6
Categorical67
Unsupported2
Boolean4

Alerts

ResponseId is highly overall correlated with TBranchHigh correlation
YearsCode is highly overall correlated with YearsCodePro and 3 other fieldsHigh correlation
YearsCodePro is highly overall correlated with YearsCode and 10 other fieldsHigh correlation
CompTotal is highly overall correlated with WorkExp and 10 other fieldsHigh correlation
WorkExp is highly overall correlated with YearsCode and 16 other fieldsHigh correlation
ConvertedCompYearly is highly overall correlated with YearsCodePro and 12 other fieldsHigh correlation
MainBranch is highly overall correlated with RemoteWork and 4 other fieldsHigh correlation
Employment is highly overall correlated with RemoteWork and 7 other fieldsHigh correlation
RemoteWork is highly overall correlated with MainBranch and 7 other fieldsHigh correlation
CodingActivities is highly overall correlated with ConvertedCompYearly and 5 other fieldsHigh correlation
EdLevel is highly overall correlated with LanguageHaveWorkedWithHigh correlation
LearnCode is highly overall correlated with WorkExp and 2 other fieldsHigh correlation
LearnCodeOnline is highly overall correlated with LearnCodeCoursesCert and 18 other fieldsHigh correlation
LearnCodeCoursesCert is highly overall correlated with CompTotal and 30 other fieldsHigh correlation
DevType is highly overall correlated with LearnCodeCoursesCert and 7 other fieldsHigh correlation
OrgSize is highly overall correlated with LearnCodeCoursesCert and 8 other fieldsHigh correlation
PurchaseInfluence is highly overall correlated with LanguageHaveWorkedWith and 3 other fieldsHigh correlation
BuyNewTool is highly overall correlated with WorkExp and 4 other fieldsHigh correlation
Country is highly overall correlated with CodingActivities and 6 other fieldsHigh correlation
Currency is highly overall correlated with Employment and 8 other fieldsHigh correlation
CompFreq is highly overall correlated with CompTotal and 8 other fieldsHigh correlation
LanguageHaveWorkedWith is highly overall correlated with YearsCode and 56 other fieldsHigh correlation
LanguageWantToWorkWith is highly overall correlated with WorkExp and 13 other fieldsHigh correlation
DatabaseHaveWorkedWith is highly overall correlated with Employment and 7 other fieldsHigh correlation
DatabaseWantToWorkWith is highly overall correlated with LanguageHaveWorkedWith and 9 other fieldsHigh correlation
PlatformHaveWorkedWith is highly overall correlated with LearnCodeOnline and 10 other fieldsHigh correlation
PlatformWantToWorkWith is highly overall correlated with YearsCodePro and 22 other fieldsHigh correlation
WebframeHaveWorkedWith is highly overall correlated with YearsCodePro and 30 other fieldsHigh correlation
WebframeWantToWorkWith is highly overall correlated with YearsCodePro and 40 other fieldsHigh correlation
MiscTechHaveWorkedWith is highly overall correlated with MainBranch and 13 other fieldsHigh correlation
MiscTechWantToWorkWith is highly overall correlated with Employment and 17 other fieldsHigh correlation
ToolsTechHaveWorkedWith is highly overall correlated with WorkExp and 8 other fieldsHigh correlation
ToolsTechWantToWorkWith is highly overall correlated with DevType and 6 other fieldsHigh correlation
NEWCollabToolsHaveWorkedWith is highly overall correlated with LanguageHaveWorkedWith and 3 other fieldsHigh correlation
NEWCollabToolsWantToWorkWith is highly overall correlated with LearnCodeOnline and 3 other fieldsHigh correlation
OpSysProfessional use is highly overall correlated with LanguageHaveWorkedWith and 2 other fieldsHigh correlation
OpSysPersonal use is highly overall correlated with ICorPM and 1 other fieldsHigh correlation
VersionControlSystem is highly overall correlated with WorkExp and 19 other fieldsHigh correlation
VCInteraction is highly overall correlated with LanguageHaveWorkedWith and 3 other fieldsHigh correlation
OfficeStackAsyncHaveWorkedWith is highly overall correlated with ConvertedCompYearly and 10 other fieldsHigh correlation
OfficeStackAsyncWantToWorkWith is highly overall correlated with MainBranch and 14 other fieldsHigh correlation
OfficeStackSyncHaveWorkedWith is highly overall correlated with RemoteWork and 6 other fieldsHigh correlation
OfficeStackSyncWantToWorkWith is highly overall correlated with RemoteWork and 11 other fieldsHigh correlation
Blockchain is highly overall correlated with ICorPM and 1 other fieldsHigh correlation
NEWSOSites is highly overall correlated with Frequency_1High correlation
SOAccount is highly overall correlated with MiscTechHaveWorkedWith and 4 other fieldsHigh correlation
SOPartFreq is highly overall correlated with WebframeWantToWorkWith and 3 other fieldsHigh correlation
SOComm is highly overall correlated with Frequency_1 and 1 other fieldsHigh correlation
Age is highly overall correlated with YearsCode and 3 other fieldsHigh correlation
Gender is highly overall correlated with LearnCodeCoursesCert and 13 other fieldsHigh correlation
Trans is highly overall correlated with YearsCodePro and 45 other fieldsHigh correlation
Sexuality is highly overall correlated with Employment and 7 other fieldsHigh correlation
Ethnicity is highly overall correlated with LanguageHaveWorkedWith and 3 other fieldsHigh correlation
Accessibility is highly overall correlated with LearnCodeCoursesCert and 11 other fieldsHigh correlation
MentalHealth is highly overall correlated with LearnCodeOnline and 5 other fieldsHigh correlation
TBranch is highly overall correlated with ResponseId and 23 other fieldsHigh correlation
ICorPM is highly overall correlated with YearsCodePro and 25 other fieldsHigh correlation
Knowledge_1 is highly overall correlated with LanguageHaveWorkedWith and 7 other fieldsHigh correlation
Knowledge_2 is highly overall correlated with LanguageHaveWorkedWith and 7 other fieldsHigh correlation
Knowledge_3 is highly overall correlated with LearnCodeOnline and 11 other fieldsHigh correlation
Knowledge_4 is highly overall correlated with LearnCodeOnline and 9 other fieldsHigh correlation
Knowledge_5 is highly overall correlated with LanguageHaveWorkedWith and 6 other fieldsHigh correlation
Knowledge_6 is highly overall correlated with LearnCodeOnline and 8 other fieldsHigh correlation
Knowledge_7 is highly overall correlated with LearnCodeOnline and 10 other fieldsHigh correlation
Frequency_1 is highly overall correlated with CompTotal and 18 other fieldsHigh correlation
Frequency_2 is highly overall correlated with LearnCodeOnline and 11 other fieldsHigh correlation
Frequency_3 is highly overall correlated with MainBranch and 10 other fieldsHigh correlation
TimeSearching is highly overall correlated with Country and 10 other fieldsHigh correlation
TimeAnswering is highly overall correlated with CompTotal and 14 other fieldsHigh correlation
Onboarding is highly overall correlated with LearnCodeOnline and 12 other fieldsHigh correlation
ProfessionalTech is highly overall correlated with LearnCodeCoursesCert and 10 other fieldsHigh correlation
TrueFalse_1 is highly overall correlated with LanguageHaveWorkedWith and 9 other fieldsHigh correlation
TrueFalse_2 is highly overall correlated with LearnCodeCoursesCert and 8 other fieldsHigh correlation
TrueFalse_3 is highly overall correlated with YearsCodePro and 13 other fieldsHigh correlation
SurveyLength is highly overall correlated with OfficeStackAsyncWantToWorkWith and 1 other fieldsHigh correlation
SurveyEase is highly overall correlated with OpSysPersonal use and 1 other fieldsHigh correlation
VersionControlSystem is highly imbalanced (70.6%)Imbalance
Gender is highly imbalanced (57.1%)Imbalance
Trans is highly imbalanced (74.3%)Imbalance
Accessibility is highly imbalanced (72.9%)Imbalance
ICorPM is highly imbalanced (67.7%)Imbalance
Employment has 2 (4.0%) missing valuesMissing
RemoteWork has 12 (24.0%) missing valuesMissing
CodingActivities has 12 (24.0%) missing valuesMissing
EdLevel has 3 (6.0%) missing valuesMissing
LearnCode has 4 (8.0%) missing valuesMissing
LearnCodeOnline has 17 (34.0%) missing valuesMissing
LearnCodeCoursesCert has 39 (78.0%) missing valuesMissing
YearsCode has 3 (6.0%) missing valuesMissing
YearsCodePro has 17 (34.0%) missing valuesMissing
DevType has 12 (24.0%) missing valuesMissing
OrgSize has 17 (34.0%) missing valuesMissing
PurchaseInfluence has 17 (34.0%) missing valuesMissing
BuyNewTool has 5 (10.0%) missing valuesMissing
Country has 2 (4.0%) missing valuesMissing
Currency has 16 (32.0%) missing valuesMissing
CompTotal has 26 (52.0%) missing valuesMissing
CompFreq has 24 (48.0%) missing valuesMissing
LanguageHaveWorkedWith has 4 (8.0%) missing valuesMissing
LanguageWantToWorkWith has 5 (10.0%) missing valuesMissing
DatabaseHaveWorkedWith has 12 (24.0%) missing valuesMissing
DatabaseWantToWorkWith has 18 (36.0%) missing valuesMissing
PlatformHaveWorkedWith has 22 (44.0%) missing valuesMissing
PlatformWantToWorkWith has 30 (60.0%) missing valuesMissing
WebframeHaveWorkedWith has 17 (34.0%) missing valuesMissing
WebframeWantToWorkWith has 22 (44.0%) missing valuesMissing
MiscTechHaveWorkedWith has 24 (48.0%) missing valuesMissing
MiscTechWantToWorkWith has 30 (60.0%) missing valuesMissing
ToolsTechHaveWorkedWith has 20 (40.0%) missing valuesMissing
ToolsTechWantToWorkWith has 24 (48.0%) missing valuesMissing
NEWCollabToolsHaveWorkedWith has 3 (6.0%) missing valuesMissing
NEWCollabToolsWantToWorkWith has 9 (18.0%) missing valuesMissing
OpSysProfessional use has 6 (12.0%) missing valuesMissing
OpSysPersonal use has 2 (4.0%) missing valuesMissing
VersionControlSystem has 2 (4.0%) missing valuesMissing
VCInteraction has 5 (10.0%) missing valuesMissing
VCHostingPersonal use has 50 (100.0%) missing valuesMissing
VCHostingProfessional use has 50 (100.0%) missing valuesMissing
OfficeStackAsyncHaveWorkedWith has 24 (48.0%) missing valuesMissing
OfficeStackAsyncWantToWorkWith has 32 (64.0%) missing valuesMissing
OfficeStackSyncHaveWorkedWith has 11 (22.0%) missing valuesMissing
OfficeStackSyncWantToWorkWith has 19 (38.0%) missing valuesMissing
Blockchain has 3 (6.0%) missing valuesMissing
NEWSOSites has 2 (4.0%) missing valuesMissing
SOVisitFreq has 2 (4.0%) missing valuesMissing
SOAccount has 2 (4.0%) missing valuesMissing
SOPartFreq has 10 (20.0%) missing valuesMissing
SOComm has 2 (4.0%) missing valuesMissing
Age has 4 (8.0%) missing valuesMissing
Gender has 4 (8.0%) missing valuesMissing
Trans has 4 (8.0%) missing valuesMissing
Sexuality has 6 (12.0%) missing valuesMissing
Ethnicity has 6 (12.0%) missing valuesMissing
Accessibility has 7 (14.0%) missing valuesMissing
MentalHealth has 7 (14.0%) missing valuesMissing
TBranch has 19 (38.0%) missing valuesMissing
ICorPM has 33 (66.0%) missing valuesMissing
WorkExp has 32 (64.0%) missing valuesMissing
Knowledge_1 has 32 (64.0%) missing valuesMissing
Knowledge_2 has 32 (64.0%) missing valuesMissing
Knowledge_3 has 32 (64.0%) missing valuesMissing
Knowledge_4 has 32 (64.0%) missing valuesMissing
Knowledge_5 has 32 (64.0%) missing valuesMissing
Knowledge_6 has 32 (64.0%) missing valuesMissing
Knowledge_7 has 32 (64.0%) missing valuesMissing
Frequency_1 has 32 (64.0%) missing valuesMissing
Frequency_2 has 32 (64.0%) missing valuesMissing
Frequency_3 has 32 (64.0%) missing valuesMissing
TimeSearching has 32 (64.0%) missing valuesMissing
TimeAnswering has 32 (64.0%) missing valuesMissing
Onboarding has 32 (64.0%) missing valuesMissing
ProfessionalTech has 33 (66.0%) missing valuesMissing
TrueFalse_1 has 33 (66.0%) missing valuesMissing
TrueFalse_2 has 32 (64.0%) missing valuesMissing
TrueFalse_3 has 32 (64.0%) missing valuesMissing
SurveyLength has 3 (6.0%) missing valuesMissing
SurveyEase has 2 (4.0%) missing valuesMissing
ConvertedCompYearly has 26 (52.0%) missing valuesMissing
ResponseId is uniformly distributedUniform
LearnCodeOnline is uniformly distributedUniform
LanguageHaveWorkedWith is uniformly distributedUniform
LanguageWantToWorkWith is uniformly distributedUniform
WebframeHaveWorkedWith is uniformly distributedUniform
WebframeWantToWorkWith is uniformly distributedUniform
ResponseId has unique valuesUnique
VCHostingPersonal use is an unsupported type, check if it needs cleaning or further analysisUnsupported
VCHostingProfessional use is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-05-21 17:49:59.310993
Analysis finished2023-05-21 17:51:11.955288
Duration1 minute and 12.64 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ResponseId
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-05-21T23:36:12.232604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.45
Q113.25
median25.5
Q337.75
95-th percentile47.55
Maximum50
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57738
Coefficient of variation (CV)0.57166195
Kurtosis-1.2
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum1275
Variance212.5
MonotonicityStrictly increasing
2023-05-21T23:36:12.632679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
38 1
 
2.0%
28 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

MainBranch
Categorical

Distinct5
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
I am a developer by profession
36 
I am not primarily a developer, but I write code sometimes as part of my work
I code primarily as a hobby
None of these
 
2
I am learning to code
 
1

Length

Max length77
Median length30
Mean length34.48
Min length13

Characters and Unicode

Total characters1724
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowNone of these
2nd rowI am a developer by profession
3rd rowI am not primarily a developer, but I write code sometimes as part of my work
4th rowI am a developer by profession
5th rowI am a developer by profession

Common Values

ValueCountFrequency (%)
I am a developer by profession 36
72.0%
I am not primarily a developer, but I write code sometimes as part of my work 6
 
12.0%
I code primarily as a hobby 5
 
10.0%
None of these 2
 
4.0%
I am learning to code 1
 
2.0%

Length

2023-05-21T23:36:12.982630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:13.306049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
i 54
15.3%
a 47
13.3%
am 43
12.2%
developer 42
11.9%
by 36
10.2%
profession 36
10.2%
code 12
 
3.4%
primarily 11
 
3.1%
as 11
 
3.1%
of 8
 
2.3%
Other values (12) 53
15.0%

Most occurring characters

ValueCountFrequency (%)
303
17.6%
e 199
11.5%
o 160
 
9.3%
a 119
 
6.9%
r 119
 
6.9%
s 97
 
5.6%
p 95
 
5.5%
m 72
 
4.2%
i 71
 
4.1%
y 58
 
3.4%
Other values (16) 431
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1359
78.8%
Space Separator 303
 
17.6%
Uppercase Letter 56
 
3.2%
Other Punctuation 6
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 199
14.6%
o 160
11.8%
a 119
 
8.8%
r 119
 
8.8%
s 97
 
7.1%
p 95
 
7.0%
m 72
 
5.3%
i 71
 
5.2%
y 58
 
4.3%
d 54
 
4.0%
Other values (12) 315
23.2%
Uppercase Letter
ValueCountFrequency (%)
I 54
96.4%
N 2
 
3.6%
Space Separator
ValueCountFrequency (%)
303
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1415
82.1%
Common 309
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 199
14.1%
o 160
11.3%
a 119
 
8.4%
r 119
 
8.4%
s 97
 
6.9%
p 95
 
6.7%
m 72
 
5.1%
i 71
 
5.0%
y 58
 
4.1%
I 54
 
3.8%
Other values (14) 371
26.2%
Common
ValueCountFrequency (%)
303
98.1%
, 6
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
303
17.6%
e 199
11.5%
o 160
 
9.3%
a 119
 
6.9%
r 119
 
6.9%
s 97
 
5.6%
p 95
 
5.5%
m 72
 
4.2%
i 71
 
4.1%
y 58
 
3.4%
Other values (16) 431
25.0%

Employment
Categorical

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)22.9%
Missing2
Missing (%)4.0%
Memory size528.0 B
Employed, full-time
25 
Student, full-time
Independent contractor, freelancer, or self-employed
Employed, full-time;Independent contractor, freelancer, or self-employed
Not employed, but looking for work
 
2
Other values (6)

Length

Max length72
Median length19
Mean length28.208333
Min length18

Characters and Unicode

Total characters1354
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)10.4%

Sample

1st rowEmployed, full-time
2nd rowEmployed, full-time
3rd rowEmployed, full-time
4th rowEmployed, full-time
5th rowStudent, full-time

Common Values

ValueCountFrequency (%)
Employed, full-time 25
50.0%
Student, full-time 6
 
12.0%
Independent contractor, freelancer, or self-employed 5
 
10.0%
Employed, full-time;Independent contractor, freelancer, or self-employed 3
 
6.0%
Not employed, but looking for work 2
 
4.0%
Student, part-time;Employed, part-time 2
 
4.0%
Student, part-time 1
 
2.0%
Employed, part-time 1
 
2.0%
Not employed, and not looking for work 1
 
2.0%
Student, full-time;Employed, part-time 1
 
2.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:13.586382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
employed 33
23.6%
full-time 31
22.1%
student 10
 
7.1%
contractor 8
 
5.7%
freelancer 8
 
5.7%
or 8
 
5.7%
self-employed 8
 
5.7%
part-time 6
 
4.3%
independent 5
 
3.6%
not 4
 
2.9%
Other values (9) 19
13.6%

Most occurring characters

ValueCountFrequency (%)
e 166
 
12.3%
l 135
 
10.0%
t 104
 
7.7%
92
 
6.8%
m 88
 
6.5%
o 84
 
6.2%
d 72
 
5.3%
, 63
 
4.7%
p 60
 
4.4%
n 56
 
4.1%
Other values (18) 434
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1085
80.1%
Space Separator 92
 
6.8%
Other Punctuation 70
 
5.2%
Uppercase Letter 55
 
4.1%
Dash Punctuation 52
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 166
15.3%
l 135
12.4%
t 104
9.6%
m 88
8.1%
o 84
 
7.7%
d 72
 
6.6%
p 60
 
5.5%
n 56
 
5.2%
f 55
 
5.1%
r 54
 
5.0%
Other values (10) 211
19.4%
Uppercase Letter
ValueCountFrequency (%)
E 33
60.0%
S 11
 
20.0%
I 8
 
14.5%
N 3
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 63
90.0%
; 7
 
10.0%
Space Separator
ValueCountFrequency (%)
92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1140
84.2%
Common 214
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 166
14.6%
l 135
11.8%
t 104
 
9.1%
m 88
 
7.7%
o 84
 
7.4%
d 72
 
6.3%
p 60
 
5.3%
n 56
 
4.9%
f 55
 
4.8%
r 54
 
4.7%
Other values (14) 266
23.3%
Common
ValueCountFrequency (%)
92
43.0%
, 63
29.4%
- 52
24.3%
; 7
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 166
 
12.3%
l 135
 
10.0%
t 104
 
7.7%
92
 
6.8%
m 88
 
6.5%
o 84
 
6.2%
d 72
 
5.3%
, 63
 
4.7%
p 60
 
4.4%
n 56
 
4.1%
Other values (18) 434
32.1%

RemoteWork
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)7.9%
Missing12
Missing (%)24.0%
Memory size528.0 B
Hybrid (some remote, some in-person)
18 
Fully remote
17 
Full in-person

Length

Max length36
Median length14
Mean length23.526316
Min length12

Characters and Unicode

Total characters894
Distinct characters21
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully remote
2nd rowHybrid (some remote, some in-person)
3rd rowFully remote
4th rowHybrid (some remote, some in-person)
5th rowHybrid (some remote, some in-person)

Common Values

ValueCountFrequency (%)
Hybrid (some remote, some in-person) 18
36.0%
Fully remote 17
34.0%
Full in-person 3
 
6.0%
(Missing) 12
24.0%

Length

2023-05-21T23:36:13.826570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:14.108745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
some 36
27.7%
remote 35
26.9%
in-person 21
16.2%
hybrid 18
13.8%
fully 17
13.1%
full 3
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 127
14.2%
o 92
10.3%
92
10.3%
r 74
 
8.3%
m 71
 
7.9%
s 57
 
6.4%
n 42
 
4.7%
l 40
 
4.5%
i 39
 
4.4%
y 35
 
3.9%
Other values (11) 225
25.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 689
77.1%
Space Separator 92
 
10.3%
Uppercase Letter 38
 
4.3%
Dash Punctuation 21
 
2.3%
Open Punctuation 18
 
2.0%
Other Punctuation 18
 
2.0%
Close Punctuation 18
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 127
18.4%
o 92
13.4%
r 74
10.7%
m 71
10.3%
s 57
8.3%
n 42
 
6.1%
l 40
 
5.8%
i 39
 
5.7%
y 35
 
5.1%
t 35
 
5.1%
Other values (4) 77
11.2%
Uppercase Letter
ValueCountFrequency (%)
F 20
52.6%
H 18
47.4%
Space Separator
ValueCountFrequency (%)
92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 727
81.3%
Common 167
 
18.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 127
17.5%
o 92
12.7%
r 74
10.2%
m 71
9.8%
s 57
7.8%
n 42
 
5.8%
l 40
 
5.5%
i 39
 
5.4%
y 35
 
4.8%
t 35
 
4.8%
Other values (6) 115
15.8%
Common
ValueCountFrequency (%)
92
55.1%
- 21
 
12.6%
( 18
 
10.8%
, 18
 
10.8%
) 18
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 127
14.2%
o 92
10.3%
92
10.3%
r 74
 
8.3%
m 71
 
7.9%
s 57
 
6.4%
n 42
 
4.7%
l 40
 
4.5%
i 39
 
4.4%
y 35
 
3.9%
Other values (11) 225
25.2%

CodingActivities
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)21.1%
Missing12
Missing (%)24.0%
Memory size528.0 B
Hobby
14 
I don’t code outside of work
11 
Hobby;Contribute to open-source projects
Hobby;Contribute to open-source projects;Bootstrapping a business
Hobby;Freelance/contract work
Other values (3)

Length

Max length65
Median length64
Mean length24.289474
Min length5

Characters and Unicode

Total characters923
Distinct characters35
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)7.9%

Sample

1st rowHobby;Contribute to open-source projects
2nd rowHobby
3rd rowI don’t code outside of work
4th rowHobby
5th rowI don’t code outside of work

Common Values

ValueCountFrequency (%)
Hobby 14
28.0%
I don’t code outside of work 11
22.0%
Hobby;Contribute to open-source projects 6
12.0%
Hobby;Contribute to open-source projects;Bootstrapping a business 2
 
4.0%
Hobby;Freelance/contract work 2
 
4.0%
Hobby;Contribute to open-source projects;Freelance/contract work 1
 
2.0%
Hobby;Bootstrapping a business 1
 
2.0%
Other (please specify): 1
 
2.0%
(Missing) 12
24.0%

Length

2023-05-21T23:36:14.354098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:14.687597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
hobby 14
10.7%
work 14
10.7%
i 11
8.4%
don’t 11
8.4%
code 11
8.4%
outside 11
8.4%
of 11
8.4%
hobby;contribute 9
6.9%
to 9
6.9%
open-source 9
6.9%
Other values (10) 21
16.0%

Most occurring characters

ValueCountFrequency (%)
o 138
15.0%
93
 
10.1%
e 74
 
8.0%
t 71
 
7.7%
b 64
 
6.9%
r 51
 
5.5%
s 43
 
4.7%
n 41
 
4.4%
c 39
 
4.2%
d 33
 
3.6%
Other values (25) 276
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 736
79.7%
Space Separator 93
 
10.1%
Uppercase Letter 53
 
5.7%
Other Punctuation 19
 
2.1%
Final Punctuation 11
 
1.2%
Dash Punctuation 9
 
1.0%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 138
18.8%
e 74
10.1%
t 71
9.6%
b 64
8.7%
r 51
 
6.9%
s 43
 
5.8%
n 41
 
5.6%
c 39
 
5.3%
d 33
 
4.5%
u 32
 
4.3%
Other values (11) 150
20.4%
Uppercase Letter
ValueCountFrequency (%)
H 26
49.1%
I 11
20.8%
C 9
 
17.0%
B 3
 
5.7%
F 3
 
5.7%
O 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
; 15
78.9%
/ 3
 
15.8%
: 1
 
5.3%
Space Separator
ValueCountFrequency (%)
93
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 789
85.5%
Common 134
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 138
17.5%
e 74
 
9.4%
t 71
 
9.0%
b 64
 
8.1%
r 51
 
6.5%
s 43
 
5.4%
n 41
 
5.2%
c 39
 
4.9%
d 33
 
4.2%
u 32
 
4.1%
Other values (17) 203
25.7%
Common
ValueCountFrequency (%)
93
69.4%
; 15
 
11.2%
’ 11
 
8.2%
- 9
 
6.7%
/ 3
 
2.2%
( 1
 
0.7%
) 1
 
0.7%
: 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 912
98.8%
Punctuation 11
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 138
15.1%
93
 
10.2%
e 74
 
8.1%
t 71
 
7.8%
b 64
 
7.0%
r 51
 
5.6%
s 43
 
4.7%
n 41
 
4.5%
c 39
 
4.3%
d 33
 
3.6%
Other values (24) 265
29.1%
Punctuation
ValueCountFrequency (%)
’ 11
100.0%

EdLevel
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)17.0%
Missing3
Missing (%)6.0%
Memory size528.0 B
Master’s degree (M.A., M.S., M.Eng., MBA, etc.)
13 
Bachelor’s degree (B.A., B.S., B.Eng., etc.)
13 
Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)
Some college/university study without earning a degree
Something else
Other values (3)

Length

Max length82
Median length54
Mean length51.085106
Min length14

Characters and Unicode

Total characters2401
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.4%

Sample

1st rowMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)
2nd rowBachelor’s degree (B.A., B.S., B.Eng., etc.)
3rd rowBachelor’s degree (B.A., B.S., B.Eng., etc.)
4th rowMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)
5th rowSecondary school (e.g. American high school, German Realschule or Gymnasium, etc.)

Common Values

ValueCountFrequency (%)
Master’s degree (M.A., M.S., M.Eng., MBA, etc.) 13
26.0%
Bachelor’s degree (B.A., B.S., B.Eng., etc.) 13
26.0%
Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 8
16.0%
Some college/university study without earning a degree 8
16.0%
Something else 2
 
4.0%
Primary/elementary school 1
 
2.0%
Other doctoral degree (Ph.D., Ed.D., etc.) 1
 
2.0%
Associate degree (A.A., A.S., etc.) 1
 
2.0%
(Missing) 3
 
6.0%

Length

2023-05-21T23:36:15.133118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:15.512990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
degree 36
 
10.9%
etc 36
 
10.9%
school 17
 
5.2%
b.eng 13
 
3.9%
b.a 13
 
3.9%
bachelor’s 13
 
3.9%
master’s 13
 
3.9%
mba 13
 
3.9%
m.eng 13
 
3.9%
m.s 13
 
3.9%
Other values (26) 150
45.5%

Most occurring characters

ValueCountFrequency (%)
283
 
11.8%
e 269
 
11.2%
. 216
 
9.0%
r 115
 
4.8%
, 111
 
4.6%
c 100
 
4.2%
g 96
 
4.0%
s 92
 
3.8%
o 92
 
3.8%
t 87
 
3.6%
Other values (25) 940
39.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1402
58.4%
Other Punctuation 336
 
14.0%
Space Separator 283
 
11.8%
Uppercase Letter 282
 
11.7%
Close Punctuation 36
 
1.5%
Open Punctuation 36
 
1.5%
Final Punctuation 26
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 269
19.2%
r 115
 
8.2%
c 100
 
7.1%
g 96
 
6.8%
s 92
 
6.6%
o 92
 
6.6%
t 87
 
6.2%
a 86
 
6.1%
n 85
 
6.1%
l 66
 
4.7%
Other values (8) 314
22.4%
Uppercase Letter
ValueCountFrequency (%)
M 65
23.0%
B 65
23.0%
A 51
18.1%
S 45
16.0%
E 27
9.6%
G 16
 
5.7%
R 8
 
2.8%
P 2
 
0.7%
D 2
 
0.7%
O 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 216
64.3%
, 111
33.0%
/ 9
 
2.7%
Space Separator
ValueCountFrequency (%)
283
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1684
70.1%
Common 717
29.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 269
16.0%
r 115
 
6.8%
c 100
 
5.9%
g 96
 
5.7%
s 92
 
5.5%
o 92
 
5.5%
t 87
 
5.2%
a 86
 
5.1%
n 85
 
5.0%
l 66
 
3.9%
Other values (18) 596
35.4%
Common
ValueCountFrequency (%)
283
39.5%
. 216
30.1%
, 111
 
15.5%
) 36
 
5.0%
( 36
 
5.0%
’ 26
 
3.6%
/ 9
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2375
98.9%
Punctuation 26
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
283
 
11.9%
e 269
 
11.3%
. 216
 
9.1%
r 115
 
4.8%
, 111
 
4.7%
c 100
 
4.2%
g 96
 
4.0%
s 92
 
3.9%
o 92
 
3.9%
t 87
 
3.7%
Other values (24) 914
38.5%
Punctuation
ValueCountFrequency (%)
’ 26
100.0%

LearnCode
Categorical

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)71.7%
Missing4
Missing (%)8.0%
Memory size528.0 B
Books / Physical media;School (i.e., University, College, etc)
Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc);On the job training
 
3
Other online resources (e.g., videos, blogs, forum)
 
3
Books / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)
 
3
Books / Physical media;Friend or family member;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)
 
2
Other values (28)
31 

Length

Max length176
Median length125.5
Mean length92.5
Min length22

Characters and Unicode

Total characters4255
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)54.3%

Sample

1st rowBooks / Physical media;Friend or family member;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)
2nd rowBooks / Physical media;School (i.e., University, College, etc)
3rd rowOther online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc);On the job training
4th rowBooks / Physical media;School (i.e., University, College, etc)
5th rowOther online resources (e.g., videos, blogs, forum)

Common Values

ValueCountFrequency (%)
Books / Physical media;School (i.e., University, College, etc) 4
 
8.0%
Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc);On the job training 3
 
6.0%
Other online resources (e.g., videos, blogs, forum) 3
 
6.0%
Books / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc) 3
 
6.0%
Books / Physical media;Friend or family member;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc) 2
 
4.0%
School (i.e., University, College, etc) 2
 
4.0%
Books / Physical media 2
 
4.0%
Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc);Online Courses or Certification 2
 
4.0%
Books / Physical media;Other online resources (e.g., videos, blogs, forum);Online Courses or Certification 1
 
2.0%
Books / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc);On the job training;Online Courses or Certification 1
 
2.0%
Other values (23) 23
46.0%
(Missing) 4
 
8.0%

Length

2023-05-21T23:36:15.868642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
online 34
 
6.5%
e.g 33
 
6.3%
blogs 33
 
6.3%
videos 33
 
6.3%
resources 33
 
6.3%
i.e 26
 
5.0%
university 26
 
5.0%
college 26
 
5.0%
23
 
4.4%
books 23
 
4.4%
Other values (36) 231
44.3%

Most occurring characters

ValueCountFrequency (%)
475
 
11.2%
e 450
 
10.6%
o 363
 
8.5%
i 287
 
6.7%
s 240
 
5.6%
r 233
 
5.5%
l 203
 
4.8%
, 177
 
4.2%
n 176
 
4.1%
t 142
 
3.3%
Other values (27) 1509
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3020
71.0%
Space Separator 475
 
11.2%
Other Punctuation 402
 
9.4%
Uppercase Letter 232
 
5.5%
Close Punctuation 63
 
1.5%
Open Punctuation 63
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 450
14.9%
o 363
12.0%
i 287
9.5%
s 240
 
7.9%
r 233
 
7.7%
l 203
 
6.7%
n 176
 
5.8%
t 142
 
4.7%
c 129
 
4.3%
g 112
 
3.7%
Other values (12) 685
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 63
27.2%
O 62
26.7%
B 26
11.2%
U 26
11.2%
S 26
11.2%
P 23
 
9.9%
F 6
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 177
44.0%
. 118
29.4%
; 80
19.9%
/ 23
 
5.7%
: 4
 
1.0%
Space Separator
ValueCountFrequency (%)
475
100.0%
Close Punctuation
ValueCountFrequency (%)
) 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3252
76.4%
Common 1003
 
23.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 450
13.8%
o 363
 
11.2%
i 287
 
8.8%
s 240
 
7.4%
r 233
 
7.2%
l 203
 
6.2%
n 176
 
5.4%
t 142
 
4.4%
c 129
 
4.0%
g 112
 
3.4%
Other values (19) 917
28.2%
Common
ValueCountFrequency (%)
475
47.4%
, 177
 
17.6%
. 118
 
11.8%
; 80
 
8.0%
) 63
 
6.3%
( 63
 
6.3%
/ 23
 
2.3%
: 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
475
 
11.2%
e 450
 
10.6%
o 363
 
8.5%
i 287
 
6.7%
s 240
 
5.6%
r 233
 
5.5%
l 203
 
4.8%
, 177
 
4.2%
n 176
 
4.1%
t 142
 
3.3%
Other values (27) 1509
35.5%

LearnCodeOnline
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct32
Distinct (%)97.0%
Missing17
Missing (%)34.0%
Memory size528.0 B
Technical documentation;Blogs;Stack Overflow
 
2
Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online books;Video-based Online Courses;Online forum;How-to videos;Written-based Online Courses
 
1
Technical documentation;Blogs
 
1
Technical documentation;Blogs;Stack Overflow;Online forum
 
1
Technical documentation;Blogs;Written Tutorials;Stack Overflow;Video-based Online Courses
 
1
Other values (27)
27 

Length

Max length239
Median length108
Mean length102.36364
Min length29

Characters and Unicode

Total characters3378
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)93.9%

Sample

1st rowTechnical documentation;Blogs;Programming Games;Written Tutorials;Stack Overflow
2nd rowTechnical documentation;Blogs;Stack Overflow;Online books;Video-based Online Courses;Online challenges (e.g., daily or weekly coding challenges)
3rd rowStack Overflow;Video-based Online Courses
4th rowTechnical documentation;Blogs;Written Tutorials;Stack Overflow;Online books;Online forum
5th rowTechnical documentation;Blogs;Written Tutorials;Stack Overflow;Video-based Online Courses;How-to videos;Written-based Online Courses

Common Values

ValueCountFrequency (%)
Technical documentation;Blogs;Stack Overflow 2
 
4.0%
Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online books;Video-based Online Courses;Online forum;How-to videos;Written-based Online Courses 1
 
2.0%
Technical documentation;Blogs 1
 
2.0%
Technical documentation;Blogs;Stack Overflow;Online forum 1
 
2.0%
Technical documentation;Blogs;Written Tutorials;Stack Overflow;Video-based Online Courses 1
 
2.0%
Technical documentation;Blogs;Written Tutorials;Stack Overflow;Video-based Online Courses;How-to videos;Auditory material (e.g., podcasts);Coding sessions (live or recorded) 1
 
2.0%
Blogs;Written Tutorials;Stack Overflow;Video-based Online Courses;Online challenges (e.g., daily or weekly coding challenges);Online forum;How-to videos;Interactive tutorial 1
 
2.0%
Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online forum;How-to videos 1
 
2.0%
Technical documentation;Online challenges (e.g., daily or weekly coding challenges);How-to videos 1
 
2.0%
Technical documentation;Blogs;Written Tutorials;Stack Overflow;Video-based Online Courses;Written-based Online Courses 1
 
2.0%
Other values (22) 22
44.0%
(Missing) 17
34.0%

Length

2023-05-21T23:36:16.148050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
technical 29
 
11.3%
online 25
 
9.8%
tutorials;stack 19
 
7.4%
documentation;blogs;written 13
 
5.1%
overflow;online 10
 
3.9%
overflow;video-based 10
 
3.9%
or 9
 
3.5%
videos;written-based 7
 
2.7%
courses 7
 
2.7%
courses;how-to 7
 
2.7%
Other values (48) 120
46.9%

Most occurring characters

ValueCountFrequency (%)
e 311
 
9.2%
o 286
 
8.5%
n 234
 
6.9%
i 225
 
6.7%
223
 
6.6%
t 209
 
6.2%
l 184
 
5.4%
s 179
 
5.3%
a 162
 
4.8%
r 148
 
4.4%
Other values (32) 1217
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2644
78.3%
Uppercase Letter 279
 
8.3%
Space Separator 223
 
6.6%
Other Punctuation 165
 
4.9%
Dash Punctuation 43
 
1.3%
Open Punctuation 12
 
0.4%
Close Punctuation 12
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 311
11.8%
o 286
10.8%
n 234
8.9%
i 225
 
8.5%
t 209
 
7.9%
l 184
 
7.0%
s 179
 
6.8%
a 162
 
6.1%
r 148
 
5.6%
c 140
 
5.3%
Other values (12) 566
21.4%
Uppercase Letter
ValueCountFrequency (%)
O 74
26.5%
T 49
17.6%
C 30
10.8%
W 29
 
10.4%
S 28
 
10.0%
B 25
 
9.0%
H 18
 
6.5%
V 16
 
5.7%
I 5
 
1.8%
A 2
 
0.7%
Other values (2) 3
 
1.1%
Other Punctuation
ValueCountFrequency (%)
; 146
88.5%
. 12
 
7.3%
, 6
 
3.6%
: 1
 
0.6%
Space Separator
ValueCountFrequency (%)
223
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2923
86.5%
Common 455
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 311
 
10.6%
o 286
 
9.8%
n 234
 
8.0%
i 225
 
7.7%
t 209
 
7.2%
l 184
 
6.3%
s 179
 
6.1%
a 162
 
5.5%
r 148
 
5.1%
c 140
 
4.8%
Other values (24) 845
28.9%
Common
ValueCountFrequency (%)
223
49.0%
; 146
32.1%
- 43
 
9.5%
( 12
 
2.6%
. 12
 
2.6%
) 12
 
2.6%
, 6
 
1.3%
: 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 311
 
9.2%
o 286
 
8.5%
n 234
 
6.9%
i 225
 
6.7%
223
 
6.6%
t 209
 
6.2%
l 184
 
5.4%
s 179
 
5.3%
a 162
 
4.8%
r 148
 
4.4%
Other values (32) 1217
36.0%

LearnCodeCoursesCert
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)81.8%
Missing39
Missing (%)78.0%
Memory size528.0 B
Udemy;Codecademy
Udemy
Coursera;Udemy
Coursera;Pluralsight
Coursera;Udemy;Codecademy;edX;Udacity
Other values (4)

Length

Max length37
Median length22
Mean length17
Min length5

Characters and Unicode

Total characters187
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)63.6%

Sample

1st rowCoursera;Udemy
2nd rowUdemy;Codecademy
3rd rowCoursera;Pluralsight
4th rowCoursera;Udemy;Codecademy;edX;Udacity
5th rowCoursera;Udemy;Pluralsight;edX

Common Values

ValueCountFrequency (%)
Udemy;Codecademy 2
 
4.0%
Udemy 2
 
4.0%
Coursera;Udemy 1
 
2.0%
Coursera;Pluralsight 1
 
2.0%
Coursera;Udemy;Codecademy;edX;Udacity 1
 
2.0%
Coursera;Udemy;Pluralsight;edX 1
 
2.0%
Other 1
 
2.0%
Coursera;Udemy;Udacity 1
 
2.0%
Udemy;Pluralsight 1
 
2.0%
(Missing) 39
78.0%

Length

2023-05-21T23:36:16.410474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:16.762856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
udemy;codecademy 2
18.2%
udemy 2
18.2%
coursera;udemy 1
9.1%
coursera;pluralsight 1
9.1%
coursera;udemy;codecademy;edx;udacity 1
9.1%
coursera;udemy;pluralsight;edx 1
9.1%
other 1
9.1%
coursera;udemy;udacity 1
9.1%
udemy;pluralsight 1
9.1%

Most occurring characters

ValueCountFrequency (%)
e 23
12.3%
d 19
 
10.2%
y 14
 
7.5%
; 14
 
7.5%
r 14
 
7.5%
a 13
 
7.0%
m 12
 
6.4%
U 11
 
5.9%
s 8
 
4.3%
u 8
 
4.3%
Other values (11) 51
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 148
79.1%
Uppercase Letter 25
 
13.4%
Other Punctuation 14
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23
15.5%
d 19
12.8%
y 14
9.5%
r 14
9.5%
a 13
8.8%
m 12
8.1%
s 8
 
5.4%
u 8
 
5.4%
o 8
 
5.4%
l 6
 
4.1%
Other values (5) 23
15.5%
Uppercase Letter
ValueCountFrequency (%)
U 11
44.0%
C 8
32.0%
P 3
 
12.0%
X 2
 
8.0%
O 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
; 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 173
92.5%
Common 14
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23
13.3%
d 19
11.0%
y 14
 
8.1%
r 14
 
8.1%
a 13
 
7.5%
m 12
 
6.9%
U 11
 
6.4%
s 8
 
4.6%
u 8
 
4.6%
o 8
 
4.6%
Other values (10) 43
24.9%
Common
ValueCountFrequency (%)
; 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23
12.3%
d 19
 
10.2%
y 14
 
7.5%
; 14
 
7.5%
r 14
 
7.5%
a 13
 
7.0%
m 12
 
6.4%
U 11
 
5.9%
s 8
 
4.3%
u 8
 
4.3%
Other values (11) 51
27.3%

YearsCode
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)51.1%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean13.021277
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-05-21T23:36:17.138575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q319.5
95-th percentile36.7
Maximum40
Range39
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation10.526342
Coefficient of variation (CV)0.80839558
Kurtosis0.67409307
Mean13.021277
Median Absolute Deviation (MAD)6
Skewness1.1449929
Sum612
Variance110.80389
MonotonicityNot monotonic
2023-05-21T23:36:17.467247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6 4
 
8.0%
4 4
 
8.0%
12 3
 
6.0%
20 3
 
6.0%
3 3
 
6.0%
7 3
 
6.0%
10 2
 
4.0%
40 2
 
4.0%
16 2
 
4.0%
5 2
 
4.0%
Other values (14) 19
38.0%
(Missing) 3
 
6.0%
ValueCountFrequency (%)
1 2
4.0%
2 2
4.0%
3 3
6.0%
4 4
8.0%
5 2
4.0%
6 4
8.0%
7 3
6.0%
8 2
4.0%
10 2
4.0%
11 1
 
2.0%
ValueCountFrequency (%)
40 2
4.0%
37 1
 
2.0%
36 1
 
2.0%
27 1
 
2.0%
25 1
 
2.0%
24 2
4.0%
22 1
 
2.0%
20 3
6.0%
19 1
 
2.0%
16 2
4.0%

YearsCodePro
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)54.5%
Missing17
Missing (%)34.0%
Infinite0
Infinite (%)0.0%
Mean11.454545
Minimum2
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-05-21T23:36:17.762630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q317
95-th percentile27
Maximum40
Range38
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.196096
Coefficient of variation (CV)0.80283378
Kurtosis1.4716573
Mean11.454545
Median Absolute Deviation (MAD)4
Skewness1.2906832
Sum378
Variance84.568182
MonotonicityNot monotonic
2023-05-21T23:36:17.998562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
5 6
 
12.0%
4 4
 
8.0%
3 3
 
6.0%
6 2
 
4.0%
10 2
 
4.0%
15 2
 
4.0%
22 2
 
4.0%
14 2
 
4.0%
30 1
 
2.0%
2 1
 
2.0%
Other values (8) 8
16.0%
(Missing) 17
34.0%
ValueCountFrequency (%)
2 1
 
2.0%
3 3
6.0%
4 4
8.0%
5 6
12.0%
6 2
 
4.0%
7 1
 
2.0%
9 1
 
2.0%
10 2
 
4.0%
14 2
 
4.0%
15 2
 
4.0%
ValueCountFrequency (%)
40 1
2.0%
30 1
2.0%
25 1
2.0%
22 2
4.0%
21 1
2.0%
20 1
2.0%
18 1
2.0%
17 1
2.0%
15 2
4.0%
14 2
4.0%

DevType
Categorical

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)63.2%
Missing12
Missing (%)24.0%
Memory size528.0 B
Developer, full-stack
Developer, full-stack;Developer, back-end
Developer, back-end
Student
 
2
Developer, front-end;Developer, full-stack;Developer, back-end;Database administrator
 
2
Other values (19)
20 

Length

Max length294
Median length109
Mean length53.5
Min length7

Characters and Unicode

Total characters2033
Distinct characters37
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)47.4%

Sample

1st rowData scientist or machine learning specialist;Developer, front-end;Engineer, data;Engineer, site reliability
2nd rowDeveloper, full-stack
3rd rowDeveloper, front-end;Developer, full-stack;Developer, back-end;Developer, desktop or enterprise applications;Developer, QA or test
4th rowDeveloper, full-stack;Student
5th rowDeveloper, back-end

Common Values

ValueCountFrequency (%)
Developer, full-stack 8
16.0%
Developer, full-stack;Developer, back-end 3
 
6.0%
Developer, back-end 3
 
6.0%
Student 2
 
4.0%
Developer, front-end;Developer, full-stack;Developer, back-end;Database administrator 2
 
4.0%
Developer, back-end;Developer, desktop or enterprise applications 2
 
4.0%
Developer, desktop or enterprise applications;Developer, mobile;Educator 1
 
2.0%
Engineering manager 1
 
2.0%
Developer, front-end 1
 
2.0%
Data scientist or machine learning specialist;Engineer, data;Developer, back-end 1
 
2.0%
Other values (14) 14
28.0%
(Missing) 12
24.0%

Length

2023-05-21T23:36:18.300512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
developer 30
17.8%
or 15
 
8.9%
full-stack;developer 9
 
5.3%
full-stack 8
 
4.7%
back-end 7
 
4.1%
desktop 6
 
3.6%
enterprise 6
 
3.6%
back-end;developer 5
 
3.0%
front-end;developer 5
 
3.0%
applications 4
 
2.4%
Other values (50) 74
43.8%

Most occurring characters

ValueCountFrequency (%)
e 313
15.4%
r 138
 
6.8%
131
 
6.4%
l 123
 
6.1%
a 120
 
5.9%
o 108
 
5.3%
t 101
 
5.0%
p 98
 
4.8%
n 97
 
4.8%
s 86
 
4.2%
Other values (27) 718
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1641
80.7%
Space Separator 131
 
6.4%
Other Punctuation 117
 
5.8%
Uppercase Letter 97
 
4.8%
Dash Punctuation 45
 
2.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 313
19.1%
r 138
 
8.4%
l 123
 
7.5%
a 120
 
7.3%
o 108
 
6.6%
t 101
 
6.2%
p 98
 
6.0%
n 97
 
5.9%
s 86
 
5.2%
i 79
 
4.8%
Other values (12) 378
23.0%
Uppercase Letter
ValueCountFrequency (%)
D 71
73.2%
E 8
 
8.2%
S 4
 
4.1%
A 4
 
4.1%
O 4
 
4.1%
C 3
 
3.1%
Q 2
 
2.1%
P 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 64
54.7%
; 52
44.4%
: 1
 
0.9%
Space Separator
ValueCountFrequency (%)
131
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1738
85.5%
Common 295
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 313
18.0%
r 138
 
7.9%
l 123
 
7.1%
a 120
 
6.9%
o 108
 
6.2%
t 101
 
5.8%
p 98
 
5.6%
n 97
 
5.6%
s 86
 
4.9%
i 79
 
4.5%
Other values (20) 475
27.3%
Common
ValueCountFrequency (%)
131
44.4%
, 64
21.7%
; 52
 
17.6%
- 45
 
15.3%
( 1
 
0.3%
) 1
 
0.3%
: 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 313
15.4%
r 138
 
6.8%
131
 
6.4%
l 123
 
6.1%
a 120
 
5.9%
o 108
 
5.3%
t 101
 
5.0%
p 98
 
4.8%
n 97
 
4.8%
s 86
 
4.2%
Other values (27) 718
35.3%

OrgSize
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)27.3%
Missing17
Missing (%)34.0%
Memory size528.0 B
20 to 99 employees
11 
100 to 499 employees
Just me - I am a freelancer, sole proprietor, etc.
2 to 9 employees
1,000 to 4,999 employees
Other values (4)

Length

Max length50
Median length24
Mean length23.878788
Min length12

Characters and Unicode

Total characters788
Distinct characters31
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)9.1%

Sample

1st row20 to 99 employees
2nd row100 to 499 employees
3rd row20 to 99 employees
4th rowI don’t know
5th rowJust me - I am a freelancer, sole proprietor, etc.

Common Values

ValueCountFrequency (%)
20 to 99 employees 11
22.0%
100 to 499 employees 7
14.0%
Just me - I am a freelancer, sole proprietor, etc. 5
 
10.0%
2 to 9 employees 3
 
6.0%
1,000 to 4,999 employees 2
 
4.0%
10,000 or more employees 2
 
4.0%
I don’t know 1
 
2.0%
5,000 to 9,999 employees 1
 
2.0%
500 to 999 employees 1
 
2.0%
(Missing) 17
34.0%

Length

2023-05-21T23:36:19.092888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:19.422862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
employees 27
16.8%
to 25
15.5%
20 11
 
6.8%
99 11
 
6.8%
100 7
 
4.3%
499 7
 
4.3%
i 6
 
3.7%
a 5
 
3.1%
etc 5
 
3.1%
sole 5
 
3.1%
Other values (19) 52
32.3%

Most occurring characters

ValueCountFrequency (%)
128
16.2%
e 118
15.0%
o 73
9.3%
9 52
 
6.6%
0 44
 
5.6%
t 41
 
5.2%
m 39
 
4.9%
s 37
 
4.7%
p 37
 
4.7%
l 37
 
4.7%
Other values (21) 182
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 488
61.9%
Decimal Number 132
 
16.8%
Space Separator 128
 
16.2%
Other Punctuation 23
 
2.9%
Uppercase Letter 11
 
1.4%
Dash Punctuation 5
 
0.6%
Final Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 118
24.2%
o 73
15.0%
t 41
 
8.4%
m 39
 
8.0%
s 37
 
7.6%
p 37
 
7.6%
l 37
 
7.6%
r 29
 
5.9%
y 27
 
5.5%
a 15
 
3.1%
Other values (8) 35
 
7.2%
Decimal Number
ValueCountFrequency (%)
9 52
39.4%
0 44
33.3%
2 14
 
10.6%
1 11
 
8.3%
4 9
 
6.8%
5 2
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 18
78.3%
. 5
 
21.7%
Uppercase Letter
ValueCountFrequency (%)
I 6
54.5%
J 5
45.5%
Space Separator
ValueCountFrequency (%)
128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 499
63.3%
Common 289
36.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 118
23.6%
o 73
14.6%
t 41
 
8.2%
m 39
 
7.8%
s 37
 
7.4%
p 37
 
7.4%
l 37
 
7.4%
r 29
 
5.8%
y 27
 
5.4%
a 15
 
3.0%
Other values (10) 46
 
9.2%
Common
ValueCountFrequency (%)
128
44.3%
9 52
18.0%
0 44
 
15.2%
, 18
 
6.2%
2 14
 
4.8%
1 11
 
3.8%
4 9
 
3.1%
. 5
 
1.7%
- 5
 
1.7%
5 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 787
99.9%
Punctuation 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
128
16.3%
e 118
15.0%
o 73
9.3%
9 52
 
6.6%
0 44
 
5.6%
t 41
 
5.2%
m 39
 
5.0%
s 37
 
4.7%
p 37
 
4.7%
l 37
 
4.7%
Other values (20) 181
23.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

PurchaseInfluence
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)9.1%
Missing17
Missing (%)34.0%
Memory size528.0 B
I have some influence
16 
I have little or no influence
I have a great deal of influence

Length

Max length32
Median length29
Mean length25.848485
Min length21

Characters and Unicode

Total characters853
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI have some influence
2nd rowI have some influence
3rd rowI have some influence
4th rowI have little or no influence
5th rowI have a great deal of influence

Common Values

ValueCountFrequency (%)
I have some influence 16
32.0%
I have little or no influence 9
18.0%
I have a great deal of influence 8
16.0%
(Missing) 17
34.0%

Length

2023-05-21T23:36:19.754750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:20.024031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
i 33
19.0%
have 33
19.0%
influence 33
19.0%
some 16
9.2%
little 9
 
5.2%
or 9
 
5.2%
no 9
 
5.2%
a 8
 
4.6%
great 8
 
4.6%
deal 8
 
4.6%

Most occurring characters

ValueCountFrequency (%)
141
16.5%
e 140
16.4%
n 75
 
8.8%
l 59
 
6.9%
a 57
 
6.7%
i 42
 
4.9%
o 42
 
4.9%
f 41
 
4.8%
c 33
 
3.9%
u 33
 
3.9%
Other values (9) 190
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 679
79.6%
Space Separator 141
 
16.5%
Uppercase Letter 33
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 140
20.6%
n 75
11.0%
l 59
8.7%
a 57
8.4%
i 42
 
6.2%
o 42
 
6.2%
f 41
 
6.0%
c 33
 
4.9%
u 33
 
4.9%
v 33
 
4.9%
Other values (7) 124
18.3%
Space Separator
ValueCountFrequency (%)
141
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 712
83.5%
Common 141
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 140
19.7%
n 75
10.5%
l 59
 
8.3%
a 57
 
8.0%
i 42
 
5.9%
o 42
 
5.9%
f 41
 
5.8%
c 33
 
4.6%
u 33
 
4.6%
I 33
 
4.6%
Other values (8) 157
22.1%
Common
ValueCountFrequency (%)
141
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
141
16.5%
e 140
16.4%
n 75
 
8.8%
l 59
 
6.9%
a 57
 
6.7%
i 42
 
4.9%
o 42
 
4.9%
f 41
 
4.8%
c 33
 
3.9%
u 33
 
3.9%
Other values (9) 190
22.3%

BuyNewTool
Categorical

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)42.2%
Missing5
Missing (%)10.0%
Memory size528.0 B
Start a free trial;Visit developer communities like Stack Overflow;Ask developers I know/work with
10 
Start a free trial
Start a free trial;Ask developers I know/work with
Start a free trial;Visit developer communities like Stack Overflow
Other (please specify):
Other values (14)
16 

Length

Max length213
Median length124
Mean length72.288889
Min length18

Characters and Unicode

Total characters3253
Distinct characters36
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)26.7%

Sample

1st rowOther (please specify):
2nd rowStart a free trial;Visit developer communities like Stack Overflow
3rd rowOther (please specify):
4th rowStart a free trial;Visit developer communities like Stack Overflow
5th rowStart a free trial

Common Values

ValueCountFrequency (%)
Start a free trial;Visit developer communities like Stack Overflow;Ask developers I know/work with 10
20.0%
Start a free trial 6
12.0%
Start a free trial;Ask developers I know/work with 5
10.0%
Start a free trial;Visit developer communities like Stack Overflow 4
 
8.0%
Other (please specify): 4
 
8.0%
Visit developer communities like Stack Overflow 2
 
4.0%
Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd 2
 
4.0%
Other (please specify):;Ask developers I know/work with 1
 
2.0%
Visit developer communities like Stack Overflow;Ask developers I know/work with;Read ratings or reviews on third party sites like G2Crowd 1
 
2.0%
Start a free trial;Visit developer communities like Stack Overflow;Ask developers I know/work with;Research companies that have advertised on sites I visit;Read ratings or reviews on third party sites like G2Crowd 1
 
2.0%
Other values (9) 9
18.0%
(Missing) 5
10.0%

Length

2023-05-21T23:36:20.277641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 32
 
6.9%
free 32
 
6.9%
like 31
 
6.7%
start 31
 
6.7%
i 27
 
5.8%
stack 24
 
5.2%
communities 24
 
5.2%
developer 24
 
5.2%
developers 23
 
5.0%
know/work 23
 
5.0%
Other values (31) 191
41.3%

Most occurring characters

ValueCountFrequency (%)
417
12.8%
e 368
 
11.3%
r 247
 
7.6%
t 246
 
7.6%
i 237
 
7.3%
o 170
 
5.2%
a 168
 
5.2%
s 160
 
4.9%
l 141
 
4.3%
k 124
 
3.8%
Other values (26) 975
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2547
78.3%
Space Separator 417
 
12.8%
Uppercase Letter 186
 
5.7%
Other Punctuation 82
 
2.5%
Open Punctuation 7
 
0.2%
Close Punctuation 7
 
0.2%
Decimal Number 7
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 368
14.4%
r 247
9.7%
t 246
9.7%
i 237
9.3%
o 170
 
6.7%
a 168
 
6.6%
s 160
 
6.3%
l 141
 
5.5%
k 124
 
4.9%
w 107
 
4.2%
Other values (11) 579
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 56
30.1%
O 31
16.7%
I 27
14.5%
V 24
12.9%
A 23
12.4%
R 11
 
5.9%
G 7
 
3.8%
C 7
 
3.8%
Other Punctuation
ValueCountFrequency (%)
; 52
63.4%
/ 23
28.0%
: 7
 
8.5%
Space Separator
ValueCountFrequency (%)
417
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Decimal Number
ValueCountFrequency (%)
2 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2733
84.0%
Common 520
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 368
13.5%
r 247
 
9.0%
t 246
 
9.0%
i 237
 
8.7%
o 170
 
6.2%
a 168
 
6.1%
s 160
 
5.9%
l 141
 
5.2%
k 124
 
4.5%
w 107
 
3.9%
Other values (19) 765
28.0%
Common
ValueCountFrequency (%)
417
80.2%
; 52
 
10.0%
/ 23
 
4.4%
( 7
 
1.3%
) 7
 
1.3%
: 7
 
1.3%
2 7
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
417
12.8%
e 368
 
11.3%
r 247
 
7.6%
t 246
 
7.6%
i 237
 
7.3%
o 170
 
5.2%
a 168
 
5.2%
s 160
 
4.9%
l 141
 
4.3%
k 124
 
3.8%
Other values (26) 975
30.0%

Country
Categorical

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)43.8%
Missing2
Missing (%)4.0%
Memory size528.0 B
United States of America
Israel
Austria
Germany
Canada
Other values (16)
20 

Length

Max length52
Median length24
Mean length12.375
Min length4

Characters and Unicode

Total characters594
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)25.0%

Sample

1st rowCanada
2nd rowUnited Kingdom of Great Britain and Northern Ireland
3rd rowIsrael
4th rowUnited States of America
5th rowGermany

Common Values

ValueCountFrequency (%)
United States of America 9
18.0%
Israel 7
14.0%
Austria 4
 
8.0%
Germany 4
 
8.0%
Canada 4
 
8.0%
Netherlands 2
 
4.0%
Czech Republic 2
 
4.0%
India 2
 
4.0%
United Kingdom of Great Britain and Northern Ireland 2
 
4.0%
Croatia 1
 
2.0%
Other values (11) 11
22.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:20.544835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 11
 
12.0%
of 11
 
12.0%
america 9
 
9.8%
states 9
 
9.8%
israel 7
 
7.6%
austria 4
 
4.3%
germany 4
 
4.3%
canada 4
 
4.3%
ireland 3
 
3.3%
great 2
 
2.2%
Other values (20) 28
30.4%

Most occurring characters

ValueCountFrequency (%)
a 74
12.5%
e 61
 
10.3%
t 45
 
7.6%
44
 
7.4%
r 44
 
7.4%
i 40
 
6.7%
n 39
 
6.6%
d 30
 
5.1%
s 26
 
4.4%
o 20
 
3.4%
Other values (27) 171
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 471
79.3%
Uppercase Letter 79
 
13.3%
Space Separator 44
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 74
15.7%
e 61
13.0%
t 45
9.6%
r 44
9.3%
i 40
8.5%
n 39
8.3%
d 30
6.4%
s 26
 
5.5%
o 20
 
4.2%
l 18
 
3.8%
Other values (13) 74
15.7%
Uppercase Letter
ValueCountFrequency (%)
A 14
17.7%
I 14
17.7%
S 12
15.2%
U 11
13.9%
C 7
8.9%
G 6
7.6%
N 5
 
6.3%
R 3
 
3.8%
B 2
 
2.5%
K 2
 
2.5%
Other values (3) 3
 
3.8%
Space Separator
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 550
92.6%
Common 44
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 74
13.5%
e 61
 
11.1%
t 45
 
8.2%
r 44
 
8.0%
i 40
 
7.3%
n 39
 
7.1%
d 30
 
5.5%
s 26
 
4.7%
o 20
 
3.6%
l 18
 
3.3%
Other values (26) 153
27.8%
Common
ValueCountFrequency (%)
44
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 74
12.5%
e 61
 
10.3%
t 45
 
7.6%
44
 
7.4%
r 44
 
7.4%
i 40
 
6.7%
n 39
 
6.6%
d 30
 
5.1%
s 26
 
4.4%
o 20
 
3.4%
Other values (27) 171
28.8%

Currency
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)29.4%
Missing16
Missing (%)32.0%
Memory size528.0 B
EUR European Euro
10 
USD United States dollar
ILS Israeli new shekel
CAD Canadian dollar
GBP Pound sterling
Other values (5)

Length

Max length24
Median length22
Mean length19.705882
Min length16

Characters and Unicode

Total characters670
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)11.8%

Sample

1st rowCAD Canadian dollar
2nd rowGBP Pound sterling
3rd rowILS Israeli new shekel
4th rowUSD United States dollar
5th rowEUR European Euro

Common Values

ValueCountFrequency (%)
EUR European Euro 10
20.0%
USD United States dollar 9
18.0%
ILS Israeli new shekel 4
 
8.0%
CAD Canadian dollar 3
 
6.0%
GBP Pound sterling 2
 
4.0%
CZK Czech koruna 2
 
4.0%
HRK Croatian kuna 1
 
2.0%
AUD Australian dollar 1
 
2.0%
RUB Russian ruble 1
 
2.0%
PLN Polish zloty 1
 
2.0%
(Missing) 16
32.0%

Length

2023-05-21T23:36:20.798411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:21.150982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
dollar 13
 
11.3%
eur 10
 
8.7%
euro 10
 
8.7%
european 10
 
8.7%
usd 9
 
7.8%
united 9
 
7.8%
states 9
 
7.8%
ils 4
 
3.5%
israeli 4
 
3.5%
new 4
 
3.5%
Other values (20) 33
28.7%

Most occurring characters

ValueCountFrequency (%)
57
 
8.5%
a 53
 
7.9%
e 49
 
7.3%
r 44
 
6.6%
l 40
 
6.0%
o 40
 
6.0%
n 39
 
5.8%
t 32
 
4.8%
E 30
 
4.5%
U 30
 
4.5%
Other values (28) 256
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 434
64.8%
Uppercase Letter 155
 
23.1%
Space Separator 57
 
8.5%
Control 24
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 53
12.2%
e 49
11.3%
r 44
10.1%
l 40
9.2%
o 40
9.2%
n 39
9.0%
t 32
7.4%
u 28
6.5%
d 27
6.2%
s 23
 
5.3%
Other values (10) 59
13.6%
Uppercase Letter
ValueCountFrequency (%)
E 30
19.4%
U 30
19.4%
S 22
14.2%
D 13
8.4%
R 13
8.4%
C 11
 
7.1%
I 8
 
5.2%
P 6
 
3.9%
L 5
 
3.2%
A 5
 
3.2%
Other values (6) 12
 
7.7%
Space Separator
ValueCountFrequency (%)
57
100.0%
Control
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 589
87.9%
Common 81
 
12.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 53
 
9.0%
e 49
 
8.3%
r 44
 
7.5%
l 40
 
6.8%
o 40
 
6.8%
n 39
 
6.6%
t 32
 
5.4%
E 30
 
5.1%
U 30
 
5.1%
u 28
 
4.8%
Other values (26) 204
34.6%
Common
ValueCountFrequency (%)
57
70.4%
24
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57
 
8.5%
a 53
 
7.9%
e 49
 
7.3%
r 44
 
6.6%
l 40
 
6.0%
o 40
 
6.0%
n 39
 
5.8%
t 32
 
4.8%
E 30
 
4.5%
U 30
 
4.5%
Other values (28) 256
38.2%

CompTotal
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)83.3%
Missing26
Missing (%)52.0%
Infinite0
Infinite (%)0.0%
Mean79823.333
Minimum400
Maximum194400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-05-21T23:36:21.488576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile4000
Q132000
median75000
Q3126250
95-th percentile181750
Maximum194400
Range194000
Interquartile range (IQR)94250

Descriptive statistics

Standard deviation56585.3
Coefficient of variation (CV)0.7088817
Kurtosis-0.70310023
Mean79823.333
Median Absolute Deviation (MAD)44000
Skewness0.37871952
Sum1915760
Variance3.2018962 × 109
MonotonicityNot monotonic
2023-05-21T23:36:21.714896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
130000 3
 
6.0%
32000 2
 
4.0%
4000 2
 
4.0%
48000 1
 
2.0%
106960 1
 
2.0%
135000 1
 
2.0%
400 1
 
2.0%
19000 1
 
2.0%
30000 1
 
2.0%
102000 1
 
2.0%
Other values (10) 10
 
20.0%
(Missing) 26
52.0%
ValueCountFrequency (%)
400 1
2.0%
4000 2
4.0%
19000 1
2.0%
30000 1
2.0%
32000 2
4.0%
37000 1
2.0%
46000 1
2.0%
48000 1
2.0%
60000 1
2.0%
65000 1
2.0%
ValueCountFrequency (%)
194400 1
 
2.0%
190000 1
 
2.0%
135000 1
 
2.0%
130000 3
6.0%
125000 1
 
2.0%
110000 1
 
2.0%
106960 1
 
2.0%
102000 1
 
2.0%
100000 1
 
2.0%
85000 1
 
2.0%

CompFreq
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)11.5%
Missing24
Missing (%)48.0%
Memory size528.0 B
Yearly
16 
Monthly
Weekly
 
1

Length

Max length7
Median length6
Mean length6.3461538
Min length6

Characters and Unicode

Total characters165
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.8%

Sample

1st rowYearly
2nd rowMonthly
3rd rowYearly
4th rowYearly
5th rowYearly

Common Values

ValueCountFrequency (%)
Yearly 16
32.0%
Monthly 9
 
18.0%
Weekly 1
 
2.0%
(Missing) 24
48.0%

Length

2023-05-21T23:36:21.954522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:22.216548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
yearly 16
61.5%
monthly 9
34.6%
weekly 1
 
3.8%

Most occurring characters

ValueCountFrequency (%)
l 26
15.8%
y 26
15.8%
e 18
10.9%
Y 16
9.7%
a 16
9.7%
r 16
9.7%
M 9
 
5.5%
o 9
 
5.5%
n 9
 
5.5%
t 9
 
5.5%
Other values (3) 11
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139
84.2%
Uppercase Letter 26
 
15.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 26
18.7%
y 26
18.7%
e 18
12.9%
a 16
11.5%
r 16
11.5%
o 9
 
6.5%
n 9
 
6.5%
t 9
 
6.5%
h 9
 
6.5%
k 1
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
Y 16
61.5%
M 9
34.6%
W 1
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 165
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 26
15.8%
y 26
15.8%
e 18
10.9%
Y 16
9.7%
a 16
9.7%
r 16
9.7%
M 9
 
5.5%
o 9
 
5.5%
n 9
 
5.5%
t 9
 
5.5%
Other values (3) 11
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 26
15.8%
y 26
15.8%
e 18
10.9%
Y 16
9.7%
a 16
9.7%
r 16
9.7%
M 9
 
5.5%
o 9
 
5.5%
n 9
 
5.5%
t 9
 
5.5%
Other values (3) 11
6.7%

LanguageHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct45
Distinct (%)97.8%
Missing4
Missing (%)8.0%
Memory size528.0 B
JavaScript;TypeScript
 
2
C#;C++;HTML/CSS;Java;JavaScript;MATLAB;Python;SQL
 
1
Bash/Shell;Groovy;HTML/CSS;Java;JavaScript;SQL
 
1
C#;HTML/CSS;JavaScript;Rust;TypeScript
 
1
C;C++
 
1
Other values (40)
40 

Length

Max length64
Median length42
Mean length31.695652
Min length3

Characters and Unicode

Total characters1458
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)95.7%

Sample

1st rowJavaScript;TypeScript
2nd rowC#;C++;HTML/CSS;JavaScript;Python
3rd rowC#;JavaScript;SQL;TypeScript
4th rowC#;HTML/CSS;JavaScript;SQL;Swift;TypeScript
5th rowC++;Lua

Common Values

ValueCountFrequency (%)
JavaScript;TypeScript 2
 
4.0%
C#;C++;HTML/CSS;Java;JavaScript;MATLAB;Python;SQL 1
 
2.0%
Bash/Shell;Groovy;HTML/CSS;Java;JavaScript;SQL 1
 
2.0%
C#;HTML/CSS;JavaScript;Rust;TypeScript 1
 
2.0%
C;C++ 1
 
2.0%
C#;JavaScript;PowerShell;SQL 1
 
2.0%
C#;HTML/CSS;JavaScript;SQL 1
 
2.0%
C++;Python 1
 
2.0%
C++;Go;Python 1
 
2.0%
HTML/CSS;JavaScript;Python;SQL;TypeScript 1
 
2.0%
Other values (35) 35
70.0%
(Missing) 4
 
8.0%

Length

2023-05-21T23:36:22.444591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
javascript;typescript 2
 
4.3%
vba 1
 
2.2%
c#;c++;html/css;javascript;python 1
 
2.2%
c#;javascript;sql;typescript 1
 
2.2%
c#;html/css;javascript;sql;swift;typescript 1
 
2.2%
c++;lua 1
 
2.2%
c++;html/css;javascript;php;python;typescript 1
 
2.2%
c;c++;html/css;java;javascript;sql 1
 
2.2%
delphi;java;swift 1
 
2.2%
bash/shell;c#;html/css;javascript;powershell;sql 1
 
2.2%
Other values (35) 35
76.1%

Most occurring characters

ValueCountFrequency (%)
; 172
 
11.8%
S 142
 
9.7%
a 101
 
6.9%
t 76
 
5.2%
p 66
 
4.5%
C 58
 
4.0%
r 58
 
4.0%
i 56
 
3.8%
L 55
 
3.8%
c 49
 
3.4%
Other values (32) 625
42.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 702
48.1%
Uppercase Letter 510
35.0%
Other Punctuation 222
 
15.2%
Math Symbol 24
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 101
14.4%
t 76
10.8%
p 66
9.4%
r 58
8.3%
i 56
8.0%
c 49
 
7.0%
v 44
 
6.3%
y 42
 
6.0%
h 40
 
5.7%
e 39
 
5.6%
Other values (11) 131
18.7%
Uppercase Letter
ValueCountFrequency (%)
S 142
27.8%
C 58
11.4%
L 55
 
10.8%
T 45
 
8.8%
J 42
 
8.2%
P 40
 
7.8%
H 34
 
6.7%
M 27
 
5.3%
Q 26
 
5.1%
B 12
 
2.4%
Other values (7) 29
 
5.7%
Other Punctuation
ValueCountFrequency (%)
; 172
77.5%
/ 33
 
14.9%
# 17
 
7.7%
Math Symbol
ValueCountFrequency (%)
+ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1212
83.1%
Common 246
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 142
 
11.7%
a 101
 
8.3%
t 76
 
6.3%
p 66
 
5.4%
C 58
 
4.8%
r 58
 
4.8%
i 56
 
4.6%
L 55
 
4.5%
c 49
 
4.0%
T 45
 
3.7%
Other values (28) 506
41.7%
Common
ValueCountFrequency (%)
; 172
69.9%
/ 33
 
13.4%
+ 24
 
9.8%
# 17
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
; 172
 
11.8%
S 142
 
9.7%
a 101
 
6.9%
t 76
 
5.2%
p 66
 
4.5%
C 58
 
4.0%
r 58
 
4.0%
i 56
 
3.8%
L 55
 
3.8%
c 49
 
3.4%
Other values (32) 625
42.9%

LanguageWantToWorkWith
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct43
Distinct (%)95.6%
Missing5
Missing (%)10.0%
Memory size528.0 B
TypeScript
 
2
Python
 
2
Rust;TypeScript
 
1
C#;Dart;Java;Kotlin;Swift;TypeScript
 
1
Bash/Shell;Groovy;HTML/CSS;Java;JavaScript;Python
 
1
Other values (38)
38 

Length

Max length79
Median length36
Mean length26.955556
Min length3

Characters and Unicode

Total characters1213
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)91.1%

Sample

1st rowRust;TypeScript
2nd rowC#;C++;HTML/CSS;JavaScript;TypeScript
3rd rowC#;SQL;TypeScript
4th rowC#;Elixir;F#;Go;JavaScript;Rust;TypeScript
5th rowLua

Common Values

ValueCountFrequency (%)
TypeScript 2
 
4.0%
Python 2
 
4.0%
Rust;TypeScript 1
 
2.0%
C#;Dart;Java;Kotlin;Swift;TypeScript 1
 
2.0%
Bash/Shell;Groovy;HTML/CSS;Java;JavaScript;Python 1
 
2.0%
HTML/CSS;Rust;TypeScript 1
 
2.0%
C++ 1
 
2.0%
Go;Python 1
 
2.0%
HTML/CSS;JavaScript;Python;SQL 1
 
2.0%
Bash/Shell;Go;Python 1
 
2.0%
Other values (33) 33
66.0%
(Missing) 5
 
10.0%

Length

2023-05-21T23:36:22.753026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
typescript 2
 
4.4%
python 2
 
4.4%
bash/shell;html/css;java;javascript;python;sql;typescript 1
 
2.2%
html/css;python;sql 1
 
2.2%
c++;go;html/css;javascript;lua;rust;typescript 1
 
2.2%
c#;c++;html/css;javascript;typescript 1
 
2.2%
c#;sql;typescript 1
 
2.2%
c#;elixir;f#;go;javascript;rust;typescript 1
 
2.2%
lua 1
 
2.2%
c;c#;c++;elixir;go;html/css;java;javascript;kotlin;python;rust;swift;typescript 1
 
2.2%
Other values (33) 33
73.3%

Most occurring characters

ValueCountFrequency (%)
; 139
 
11.5%
S 104
 
8.6%
t 74
 
6.1%
a 72
 
5.9%
p 56
 
4.6%
i 55
 
4.5%
r 49
 
4.0%
y 44
 
3.6%
l 42
 
3.5%
h 39
 
3.2%
Other values (33) 539
44.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 649
53.5%
Uppercase Letter 372
30.7%
Other Punctuation 176
 
14.5%
Math Symbol 16
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 74
11.4%
a 72
11.1%
p 56
 
8.6%
i 55
 
8.5%
r 49
 
7.6%
y 44
 
6.8%
l 42
 
6.5%
h 39
 
6.0%
c 38
 
5.9%
o 38
 
5.9%
Other values (11) 142
21.9%
Uppercase Letter
ValueCountFrequency (%)
S 104
28.0%
L 38
 
10.2%
T 36
 
9.7%
C 35
 
9.4%
P 26
 
7.0%
J 24
 
6.5%
H 20
 
5.4%
Q 18
 
4.8%
M 16
 
4.3%
B 11
 
3.0%
Other values (8) 44
11.8%
Other Punctuation
ValueCountFrequency (%)
; 139
79.0%
/ 25
 
14.2%
# 12
 
6.8%
Math Symbol
ValueCountFrequency (%)
+ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1021
84.2%
Common 192
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 104
 
10.2%
t 74
 
7.2%
a 72
 
7.1%
p 56
 
5.5%
i 55
 
5.4%
r 49
 
4.8%
y 44
 
4.3%
l 42
 
4.1%
h 39
 
3.8%
c 38
 
3.7%
Other values (29) 448
43.9%
Common
ValueCountFrequency (%)
; 139
72.4%
/ 25
 
13.0%
+ 16
 
8.3%
# 12
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1213
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
; 139
 
11.5%
S 104
 
8.6%
t 74
 
6.1%
a 72
 
5.9%
p 56
 
4.6%
i 55
 
4.5%
r 49
 
4.0%
y 44
 
3.6%
l 42
 
3.5%
h 39
 
3.2%
Other values (33) 539
44.4%

DatabaseHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)71.1%
Missing12
Missing (%)24.0%
Memory size528.0 B
Microsoft SQL Server
PostgreSQL
MongoDB;MySQL
MySQL
PostgreSQL;Redis;SQLite
 
1
Other values (22)
22 

Length

Max length84
Median length44.5
Mean length25.710526
Min length5

Characters and Unicode

Total characters977
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)60.5%

Sample

1st rowMicrosoft SQL Server
2nd rowMicrosoft SQL Server
3rd rowCloud Firestore;Elasticsearch;Microsoft SQL Server;Firebase Realtime Database
4th rowCloud Firestore;MongoDB;Firebase Realtime Database
5th rowMongoDB;MySQL

Common Values

ValueCountFrequency (%)
Microsoft SQL Server 5
 
10.0%
PostgreSQL 4
 
8.0%
MongoDB;MySQL 3
 
6.0%
MySQL 3
 
6.0%
PostgreSQL;Redis;SQLite 1
 
2.0%
MongoDB;SQLite 1
 
2.0%
Microsoft SQL Server;MongoDB;MySQL;Oracle;PostgreSQL 1
 
2.0%
MongoDB;MySQL;SQLite 1
 
2.0%
Elasticsearch;MariaDB;PostgreSQL;Redis 1
 
2.0%
SQLite 1
 
2.0%
Other values (17) 17
34.0%
(Missing) 12
24.0%

Length

2023-05-21T23:36:23.046069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sql 13
17.8%
microsoft 10
 
13.7%
server 5
 
6.8%
postgresql 4
 
5.5%
realtime 3
 
4.1%
cloud 3
 
4.1%
mysql 3
 
4.1%
mongodb;mysql 3
 
4.1%
database 2
 
2.7%
server;firebase 1
 
1.4%
Other values (26) 26
35.6%

Most occurring characters

ValueCountFrequency (%)
e 86
 
8.8%
o 76
 
7.8%
r 74
 
7.6%
S 67
 
6.9%
s 58
 
5.9%
; 55
 
5.6%
Q 54
 
5.5%
L 54
 
5.5%
t 51
 
5.2%
i 45
 
4.6%
Other values (26) 357
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 584
59.8%
Uppercase Letter 302
30.9%
Other Punctuation 55
 
5.6%
Space Separator 35
 
3.6%
Decimal Number 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 86
14.7%
o 76
13.0%
r 74
12.7%
s 58
9.9%
t 51
8.7%
i 45
7.7%
a 38
6.5%
g 28
 
4.8%
c 24
 
4.1%
y 17
 
2.9%
Other values (10) 87
14.9%
Uppercase Letter
ValueCountFrequency (%)
S 67
22.2%
Q 54
17.9%
L 54
17.9%
M 44
14.6%
D 21
 
7.0%
B 17
 
5.6%
P 16
 
5.3%
R 10
 
3.3%
C 7
 
2.3%
F 6
 
2.0%
Other values (3) 6
 
2.0%
Other Punctuation
ValueCountFrequency (%)
; 55
100.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 886
90.7%
Common 91
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 86
 
9.7%
o 76
 
8.6%
r 74
 
8.4%
S 67
 
7.6%
s 58
 
6.5%
Q 54
 
6.1%
L 54
 
6.1%
t 51
 
5.8%
i 45
 
5.1%
M 44
 
5.0%
Other values (23) 277
31.3%
Common
ValueCountFrequency (%)
; 55
60.4%
35
38.5%
4 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 86
 
8.8%
o 76
 
7.8%
r 74
 
7.6%
S 67
 
6.9%
s 58
 
5.9%
; 55
 
5.6%
Q 54
 
5.5%
L 54
 
5.5%
t 51
 
5.2%
i 45
 
4.6%
Other values (26) 357
36.5%

DatabaseWantToWorkWith
Categorical

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)78.1%
Missing18
Missing (%)36.0%
Memory size528.0 B
Microsoft SQL Server
SQLite
PostgreSQL;Redis;SQLite
 
2
MySQL
 
2
MySQL;PostgreSQL;Redis;SQLite
 
1
Other values (20)
20 

Length

Max length84
Median length38
Mean length26.5625
Min length5

Characters and Unicode

Total characters850
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)65.6%

Sample

1st rowMicrosoft SQL Server
2nd rowMicrosoft SQL Server
3rd rowCloud Firestore;Elasticsearch;Firebase Realtime Database;Redis
4th rowMySQL;Oracle;PostgreSQL
5th rowNeo4j;PostgreSQL

Common Values

ValueCountFrequency (%)
Microsoft SQL Server 4
 
8.0%
SQLite 3
 
6.0%
PostgreSQL;Redis;SQLite 2
 
4.0%
MySQL 2
 
4.0%
MySQL;PostgreSQL;Redis;SQLite 1
 
2.0%
MongoDB;MySQL;PostgreSQL;Firebase Realtime Database 1
 
2.0%
DynamoDB;Elasticsearch;Microsoft SQL Server;MongoDB;MySQL;PostgreSQL;Redis 1
 
2.0%
PostgreSQL;Redis 1
 
2.0%
Elasticsearch;MariaDB;PostgreSQL;Redis 1
 
2.0%
CouchDB;Elasticsearch;MySQL;PostgreSQL;Redis 1
 
2.0%
Other values (15) 15
30.0%
(Missing) 18
36.0%

Length

2023-05-21T23:36:23.314073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sql 7
 
12.5%
microsoft 5
 
8.9%
server 4
 
7.1%
realtime 4
 
7.1%
sqlite 3
 
5.4%
postgresql;redis;sqlite 2
 
3.6%
mysql 2
 
3.6%
database 2
 
3.6%
cloud 2
 
3.6%
postgresql 1
 
1.8%
Other values (24) 24
42.9%

Most occurring characters

ValueCountFrequency (%)
e 84
 
9.9%
s 62
 
7.3%
r 59
 
6.9%
o 53
 
6.2%
t 50
 
5.9%
; 49
 
5.8%
S 48
 
5.6%
a 47
 
5.5%
i 46
 
5.4%
Q 41
 
4.8%
Other values (26) 311
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 538
63.3%
Uppercase Letter 237
27.9%
Other Punctuation 49
 
5.8%
Space Separator 24
 
2.8%
Decimal Number 2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 84
15.6%
s 62
11.5%
r 59
11.0%
o 53
9.9%
t 50
9.3%
a 47
8.7%
i 46
8.6%
c 25
 
4.6%
g 24
 
4.5%
l 15
 
2.8%
Other values (10) 73
13.6%
Uppercase Letter
ValueCountFrequency (%)
S 48
20.3%
Q 41
17.3%
L 41
17.3%
M 26
11.0%
D 17
 
7.2%
P 17
 
7.2%
R 14
 
5.9%
B 12
 
5.1%
E 8
 
3.4%
F 6
 
2.5%
Other values (3) 7
 
3.0%
Other Punctuation
ValueCountFrequency (%)
; 49
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Decimal Number
ValueCountFrequency (%)
4 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 775
91.2%
Common 75
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 84
 
10.8%
s 62
 
8.0%
r 59
 
7.6%
o 53
 
6.8%
t 50
 
6.5%
S 48
 
6.2%
a 47
 
6.1%
i 46
 
5.9%
Q 41
 
5.3%
L 41
 
5.3%
Other values (23) 244
31.5%
Common
ValueCountFrequency (%)
; 49
65.3%
24
32.0%
4 2
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 84
 
9.9%
s 62
 
7.3%
r 59
 
6.9%
o 53
 
6.2%
t 50
 
5.9%
; 49
 
5.8%
S 48
 
5.6%
a 47
 
5.5%
i 46
 
5.4%
Q 41
 
4.8%
Other values (26) 311
36.6%

PlatformHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)71.4%
Missing22
Missing (%)44.0%
Memory size528.0 B
AWS;Microsoft Azure
Microsoft Azure
AWS
AWS;Google Cloud
DigitalOcean;Firebase
 
1
Other values (15)
15 

Length

Max length35
Median length25.5
Mean length17.785714
Min length3

Characters and Unicode

Total characters498
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)57.1%

Sample

1st rowFirebase;Microsoft Azure
2nd rowAWS;Google Cloud;Heroku
3rd rowDigitalOcean;Firebase
4th rowAWS;Microsoft Azure
5th rowAWS;Microsoft Azure

Common Values

ValueCountFrequency (%)
AWS;Microsoft Azure 4
 
8.0%
Microsoft Azure 3
 
6.0%
AWS 3
 
6.0%
AWS;Google Cloud 2
 
4.0%
DigitalOcean;Firebase 1
 
2.0%
AWS;Heroku 1
 
2.0%
AWS;DigitalOcean;Heroku 1
 
2.0%
AWS;Firebase;Google Cloud;Heroku 1
 
2.0%
Google Cloud;VMware 1
 
2.0%
Firebase;Microsoft Azure 1
 
2.0%
Other values (10) 10
20.0%
(Missing) 22
44.0%

Length

2023-05-21T23:36:23.560058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
azure 10
20.8%
aws;microsoft 4
 
8.3%
aws;google 4
 
8.3%
microsoft 4
 
8.3%
google 3
 
6.2%
aws 3
 
6.2%
cloud 2
 
4.2%
cloud;ovh;vmware 2
 
4.2%
cloud;heroku 2
 
4.2%
aws;managed 1
 
2.1%
Other values (13) 13
27.1%

Most occurring characters

ValueCountFrequency (%)
o 54
 
10.8%
e 42
 
8.4%
r 37
 
7.4%
; 31
 
6.2%
A 29
 
5.8%
u 25
 
5.0%
i 21
 
4.2%
20
 
4.0%
S 18
 
3.6%
l 18
 
3.6%
Other values (22) 203
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 319
64.1%
Uppercase Letter 128
25.7%
Other Punctuation 31
 
6.2%
Space Separator 20
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 54
16.9%
e 42
13.2%
r 37
11.6%
u 25
 
7.8%
i 21
 
6.6%
l 18
 
5.6%
s 16
 
5.0%
a 15
 
4.7%
t 14
 
4.4%
c 13
 
4.1%
Other values (8) 64
20.1%
Uppercase Letter
ValueCountFrequency (%)
A 29
22.7%
S 18
14.1%
W 18
14.1%
M 17
13.3%
H 10
 
7.8%
V 8
 
6.2%
G 8
 
6.2%
C 8
 
6.2%
O 5
 
3.9%
F 4
 
3.1%
Other values (2) 3
 
2.3%
Other Punctuation
ValueCountFrequency (%)
; 31
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 447
89.8%
Common 51
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 54
 
12.1%
e 42
 
9.4%
r 37
 
8.3%
A 29
 
6.5%
u 25
 
5.6%
i 21
 
4.7%
S 18
 
4.0%
l 18
 
4.0%
W 18
 
4.0%
M 17
 
3.8%
Other values (20) 168
37.6%
Common
ValueCountFrequency (%)
; 31
60.8%
20
39.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 54
 
10.8%
e 42
 
8.4%
r 37
 
7.4%
; 31
 
6.2%
A 29
 
5.8%
u 25
 
5.0%
i 21
 
4.2%
20
 
4.0%
S 18
 
3.6%
l 18
 
3.6%
Other values (22) 203
40.8%

PlatformWantToWorkWith
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)70.0%
Missing30
Missing (%)60.0%
Memory size528.0 B
AWS
Google Cloud;OVH;VMware
Firebase;Microsoft Azure
DigitalOcean;Firebase;Microsoft Azure;VMware
DigitalOcean;Firebase
Other values (9)

Length

Max length44
Median length29
Mean length17.75
Min length3

Characters and Unicode

Total characters355
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)60.0%

Sample

1st rowFirebase;Microsoft Azure
2nd rowDigitalOcean;Firebase;Microsoft Azure;VMware
3rd rowDigitalOcean;Firebase
4th rowMicrosoft Azure
5th rowAWS;DigitalOcean;Firebase;Linode

Common Values

ValueCountFrequency (%)
AWS 6
 
12.0%
Google Cloud;OVH;VMware 2
 
4.0%
Firebase;Microsoft Azure 1
 
2.0%
DigitalOcean;Firebase;Microsoft Azure;VMware 1
 
2.0%
DigitalOcean;Firebase 1
 
2.0%
Microsoft Azure 1
 
2.0%
AWS;DigitalOcean;Firebase;Linode 1
 
2.0%
AWS;Firebase;Google Cloud;Heroku 1
 
2.0%
AWS;Google Cloud 1
 
2.0%
AWS;Microsoft Azure 1
 
2.0%
Other values (4) 4
 
8.0%
(Missing) 30
60.0%

Length

2023-05-21T23:36:23.794011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aws 6
19.4%
azure 5
16.1%
cloud;ovh;vmware 2
 
6.5%
google 2
 
6.5%
aws;google 2
 
6.5%
cloud;heroku 1
 
3.2%
vmware 1
 
3.2%
aws;heroku;linode 1
 
3.2%
cloud;linode;microsoft 1
 
3.2%
aws;microsoft 1
 
3.2%
Other values (9) 9
29.0%

Most occurring characters

ValueCountFrequency (%)
e 34
 
9.6%
o 33
 
9.3%
r 24
 
6.8%
; 24
 
6.8%
i 20
 
5.6%
A 19
 
5.4%
a 15
 
4.2%
u 14
 
3.9%
W 13
 
3.7%
S 13
 
3.7%
Other values (22) 146
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228
64.2%
Uppercase Letter 92
25.9%
Other Punctuation 24
 
6.8%
Space Separator 11
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34
14.9%
o 33
14.5%
r 24
10.5%
i 20
8.8%
a 15
 
6.6%
u 14
 
6.1%
l 13
 
5.7%
s 11
 
4.8%
t 9
 
3.9%
c 9
 
3.9%
Other values (8) 46
20.2%
Uppercase Letter
ValueCountFrequency (%)
A 19
20.7%
W 13
14.1%
S 13
14.1%
M 10
10.9%
V 6
 
6.5%
H 5
 
5.4%
G 5
 
5.4%
F 5
 
5.4%
C 5
 
5.4%
O 5
 
5.4%
Other values (2) 6
 
6.5%
Other Punctuation
ValueCountFrequency (%)
; 24
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 320
90.1%
Common 35
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34
 
10.6%
o 33
 
10.3%
r 24
 
7.5%
i 20
 
6.2%
A 19
 
5.9%
a 15
 
4.7%
u 14
 
4.4%
W 13
 
4.1%
S 13
 
4.1%
l 13
 
4.1%
Other values (20) 122
38.1%
Common
ValueCountFrequency (%)
; 24
68.6%
11
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34
 
9.6%
o 33
 
9.3%
r 24
 
6.8%
; 24
 
6.8%
i 20
 
5.6%
A 19
 
5.4%
a 15
 
4.2%
u 14
 
3.9%
W 13
 
3.7%
S 13
 
3.7%
Other values (22) 146
41.1%

WebframeHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct30
Distinct (%)90.9%
Missing17
Missing (%)34.0%
Memory size528.0 B
ASP.NET;ASP.NET Core
 
2
jQuery;Node.js
 
2
Node.js
 
2
Flask;jQuery;Node.js;Nuxt.js;React.js;Ruby on Rails;Vue.js
 
1
ASP.NET Core
 
1
Other values (25)
25 

Length

Max length58
Median length34
Mean length25.272727
Min length5

Characters and Unicode

Total characters834
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)81.8%

Sample

1st rowAngular.js
2nd rowASP.NET;ASP.NET Core
3rd rowAngular;ASP.NET;ASP.NET Core ;jQuery;Node.js
4th rowAngular;Next.js;Node.js;React.js;Svelte;Vue.js
5th rowjQuery;Node.js

Common Values

ValueCountFrequency (%)
ASP.NET;ASP.NET Core 2
 
4.0%
jQuery;Node.js 2
 
4.0%
Node.js 2
 
4.0%
Flask;jQuery;Node.js;Nuxt.js;React.js;Ruby on Rails;Vue.js 1
 
2.0%
ASP.NET Core 1
 
2.0%
ASP.NET 1
 
2.0%
Django;FastAPI;React.js 1
 
2.0%
Angular;Angular.js;Django;Flask;jQuery;Node.js 1
 
2.0%
ASP.NET;ASP.NET Core ;jQuery 1
 
2.0%
Flask 1
 
2.0%
Other values (20) 20
40.0%
(Missing) 17
34.0%

Length

2023-05-21T23:36:24.044796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
core 11
18.6%
asp.net;asp.net 5
 
8.5%
on 4
 
6.8%
asp.net 4
 
6.8%
jquery;node.js 3
 
5.1%
react.js 3
 
5.1%
rails 2
 
3.4%
angular;asp.net 2
 
3.4%
jquery 2
 
3.4%
rails;vue.js 2
 
3.4%
Other values (20) 21
35.6%

Most occurring characters

ValueCountFrequency (%)
e 67
 
8.0%
; 66
 
7.9%
. 59
 
7.1%
s 58
 
7.0%
j 56
 
6.7%
r 38
 
4.6%
a 37
 
4.4%
N 37
 
4.4%
u 37
 
4.4%
o 35
 
4.2%
Other values (29) 344
41.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 469
56.2%
Uppercase Letter 210
25.2%
Other Punctuation 125
 
15.0%
Space Separator 30
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 67
14.3%
s 58
12.4%
j 56
11.9%
r 38
8.1%
a 37
7.9%
u 37
7.9%
o 35
7.5%
l 21
 
4.5%
n 19
 
4.1%
t 17
 
3.6%
Other values (11) 84
17.9%
Uppercase Letter
ValueCountFrequency (%)
N 37
17.6%
A 31
14.8%
E 21
10.0%
P 21
10.0%
S 19
9.0%
R 18
8.6%
T 18
8.6%
Q 12
 
5.7%
C 11
 
5.2%
V 9
 
4.3%
Other values (5) 13
 
6.2%
Other Punctuation
ValueCountFrequency (%)
; 66
52.8%
. 59
47.2%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 679
81.4%
Common 155
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 67
 
9.9%
s 58
 
8.5%
j 56
 
8.2%
r 38
 
5.6%
a 37
 
5.4%
N 37
 
5.4%
u 37
 
5.4%
o 35
 
5.2%
A 31
 
4.6%
E 21
 
3.1%
Other values (26) 262
38.6%
Common
ValueCountFrequency (%)
; 66
42.6%
. 59
38.1%
30
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 67
 
8.0%
; 66
 
7.9%
. 59
 
7.1%
s 58
 
7.0%
j 56
 
6.7%
r 38
 
4.6%
a 37
 
4.4%
N 37
 
4.4%
u 37
 
4.4%
o 35
 
4.2%
Other values (29) 344
41.2%

WebframeWantToWorkWith
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct27
Distinct (%)96.4%
Missing22
Missing (%)44.0%
Memory size528.0 B
React.js
 
2
ASP.NET Core
 
1
Node.js;React.js;Vue.js
 
1
Angular;Angular.js;ASP.NET Core ;Blazor;Django;Node.js;React.js;Ruby on Rails
 
1
Vue.js
 
1
Other values (22)
22 

Length

Max length77
Median length35.5
Mean length26.607143
Min length6

Characters and Unicode

Total characters745
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st rowAngular;Angular.js
2nd rowASP.NET;ASP.NET Core
3rd rowAngular;ASP.NET Core ;Blazor;Node.js
4th rowDjango;Flask;Gatsby;jQuery;Next.js;Node.js;React.js;Svelte;Vue.js
5th rowAngular;Angular.js;Next.js;Vue.js

Common Values

ValueCountFrequency (%)
React.js 2
 
4.0%
ASP.NET Core 1
 
2.0%
Node.js;React.js;Vue.js 1
 
2.0%
Angular;Angular.js;ASP.NET Core ;Blazor;Django;Node.js;React.js;Ruby on Rails 1
 
2.0%
Vue.js 1
 
2.0%
Flask;Ruby on Rails 1
 
2.0%
jQuery;Node.js;Nuxt.js;Ruby on Rails;Vue.js 1
 
2.0%
Django;FastAPI;React.js 1
 
2.0%
Django 1
 
2.0%
Angular;ASP.NET Core ;Blazor;Vue.js 1
 
2.0%
Other values (17) 17
34.0%
(Missing) 22
44.0%

Length

2023-05-21T23:36:24.314416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
core 8
 
16.3%
on 4
 
8.2%
angular;asp.net 3
 
6.1%
react.js 2
 
4.1%
rails 2
 
4.1%
asp.net;asp.net 2
 
4.1%
asp.net 2
 
4.1%
blazor 2
 
4.1%
vue.js 1
 
2.0%
flask;ruby 1
 
2.0%
Other values (22) 22
44.9%

Most occurring characters

ValueCountFrequency (%)
; 61
 
8.2%
s 57
 
7.7%
e 54
 
7.2%
. 53
 
7.1%
j 51
 
6.8%
a 41
 
5.5%
o 34
 
4.6%
u 32
 
4.3%
N 28
 
3.8%
r 28
 
3.8%
Other values (32) 306
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 447
60.0%
Uppercase Letter 160
 
21.5%
Other Punctuation 114
 
15.3%
Space Separator 24
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 57
12.8%
e 54
12.1%
j 51
11.4%
a 41
9.2%
o 34
7.6%
u 32
 
7.2%
r 28
 
6.3%
t 25
 
5.6%
l 23
 
5.1%
n 21
 
4.7%
Other values (14) 81
18.1%
Uppercase Letter
ValueCountFrequency (%)
N 28
17.5%
A 23
14.4%
R 19
11.9%
P 14
8.8%
S 13
8.1%
E 12
7.5%
V 11
 
6.9%
T 10
 
6.2%
C 8
 
5.0%
D 5
 
3.1%
Other values (5) 17
10.6%
Other Punctuation
ValueCountFrequency (%)
; 61
53.5%
. 53
46.5%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 607
81.5%
Common 138
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 57
 
9.4%
e 54
 
8.9%
j 51
 
8.4%
a 41
 
6.8%
o 34
 
5.6%
u 32
 
5.3%
N 28
 
4.6%
r 28
 
4.6%
t 25
 
4.1%
l 23
 
3.8%
Other values (29) 234
38.6%
Common
ValueCountFrequency (%)
; 61
44.2%
. 53
38.4%
24
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
; 61
 
8.2%
s 57
 
7.7%
e 54
 
7.2%
. 53
 
7.1%
j 51
 
6.8%
a 41
 
5.5%
o 34
 
4.6%
u 32
 
4.3%
N 28
 
3.8%
r 28
 
3.8%
Other values (32) 306
41.1%

MiscTechHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)65.4%
Missing24
Missing (%)48.0%
Memory size528.0 B
.NET
10 
Qt
 
1
.NET;NumPy;Spring;Torch/PyTorch
 
1
Keras;NumPy;Pandas;Scikit-learn;TensorFlow
 
1
.NET;Xamarin
 
1
Other values (12)
12 

Length

Max length87
Median length48.5
Mean length17.615385
Min length2

Characters and Unicode

Total characters458
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)61.5%

Sample

1st rowPandas
2nd row.NET
3rd row.NET
4th row.NET
5th row.NET;Keras;NumPy;Pandas;Scikit-learn;TensorFlow;Torch/PyTorch;Hugging Face Transformers

Common Values

ValueCountFrequency (%)
.NET 10
20.0%
Qt 1
 
2.0%
.NET;NumPy;Spring;Torch/PyTorch 1
 
2.0%
Keras;NumPy;Pandas;Scikit-learn;TensorFlow 1
 
2.0%
.NET;Xamarin 1
 
2.0%
Spring 1
 
2.0%
NumPy;Scikit-learn;Torch/PyTorch 1
 
2.0%
NumPy;Pandas;Scikit-learn 1
 
2.0%
Apache Kafka;Flutter 1
 
2.0%
Pandas 1
 
2.0%
Other values (7) 7
 
14.0%
(Missing) 24
48.0%

Length

2023-05-21T23:36:24.574014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
net 10
30.3%
apache 2
 
6.1%
native 2
 
6.1%
keras;numpy;pandas;scikit-learn;spring;tensorflow 1
 
3.0%
transformers 1
 
3.0%
face 1
 
3.0%
net;keras;numpy;pandas;scikit-learn;tensorflow;torch/pytorch;hugging 1
 
3.0%
torch/pytorch 1
 
3.0%
spark;numpy;pandas;tidyverse 1
 
3.0%
kafka;apache 1
 
3.0%
Other values (12) 12
36.4%

Most occurring characters

ValueCountFrequency (%)
a 38
 
8.3%
; 31
 
6.8%
r 30
 
6.6%
T 26
 
5.7%
e 24
 
5.2%
N 23
 
5.0%
n 22
 
4.8%
c 20
 
4.4%
P 19
 
4.1%
i 18
 
3.9%
Other values (28) 207
45.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 289
63.1%
Uppercase Letter 109
 
23.8%
Other Punctuation 48
 
10.5%
Space Separator 7
 
1.5%
Dash Punctuation 5
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 38
13.1%
r 30
 
10.4%
e 24
 
8.3%
n 22
 
7.6%
c 20
 
6.9%
i 18
 
6.2%
o 16
 
5.5%
s 16
 
5.5%
t 13
 
4.5%
y 13
 
4.5%
Other values (11) 79
27.3%
Uppercase Letter
ValueCountFrequency (%)
T 26
23.9%
N 23
21.1%
P 19
17.4%
E 14
12.8%
S 9
 
8.3%
F 5
 
4.6%
K 5
 
4.6%
A 3
 
2.8%
R 2
 
1.8%
Q 1
 
0.9%
Other values (2) 2
 
1.8%
Other Punctuation
ValueCountFrequency (%)
; 31
64.6%
. 13
27.1%
/ 4
 
8.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 398
86.9%
Common 60
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 38
 
9.5%
r 30
 
7.5%
T 26
 
6.5%
e 24
 
6.0%
N 23
 
5.8%
n 22
 
5.5%
c 20
 
5.0%
P 19
 
4.8%
i 18
 
4.5%
o 16
 
4.0%
Other values (23) 162
40.7%
Common
ValueCountFrequency (%)
; 31
51.7%
. 13
21.7%
7
 
11.7%
- 5
 
8.3%
/ 4
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 38
 
8.3%
; 31
 
6.8%
r 30
 
6.6%
T 26
 
5.7%
e 24
 
5.2%
N 23
 
5.0%
n 22
 
4.8%
c 20
 
4.4%
P 19
 
4.1%
i 18
 
3.9%
Other values (28) 207
45.2%

MiscTechWantToWorkWith
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)75.0%
Missing30
Missing (%)60.0%
Memory size528.0 B
.NET
Flutter
.NET;Apache Kafka
 
1
Keras;NumPy;Pandas;Scikit-learn;TensorFlow;Torch/PyTorch;Hugging Face Transformers
 
1
Torch/PyTorch
 
1
Other values (10)
10 

Length

Max length82
Median length39.5
Mean length19.9
Min length4

Characters and Unicode

Total characters398
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)65.0%

Sample

1st row.NET
2nd row.NET
3rd row.NET;Apache Kafka
4th rowKeras;NumPy;Pandas;Scikit-learn;TensorFlow;Torch/PyTorch;Hugging Face Transformers
5th rowTorch/PyTorch

Common Values

ValueCountFrequency (%)
.NET 4
 
8.0%
Flutter 3
 
6.0%
.NET;Apache Kafka 1
 
2.0%
Keras;NumPy;Pandas;Scikit-learn;TensorFlow;Torch/PyTorch;Hugging Face Transformers 1
 
2.0%
Torch/PyTorch 1
 
2.0%
Pandas 1
 
2.0%
Electron 1
 
2.0%
Keras;NumPy;Pandas;Scikit-learn;TensorFlow 1
 
2.0%
NumPy;Pandas 1
 
2.0%
Apache Kafka;Flutter 1
 
2.0%
Other values (5) 5
 
10.0%
(Missing) 30
60.0%

Length

2023-05-21T23:36:24.828830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
net 4
 
14.8%
flutter 3
 
11.1%
face 2
 
7.4%
transformers 2
 
7.4%
apache 1
 
3.7%
native 1
 
3.7%
net;react 1
 
3.7%
keras;numpy;pandas;scikit-learn;tensorflow;hugging 1
 
3.7%
numpy;scikit-learn;torch/pytorch 1
 
3.7%
xamarin 1
 
3.7%
Other values (10) 10
37.0%

Most occurring characters

ValueCountFrequency (%)
r 32
 
8.0%
a 31
 
7.8%
e 24
 
6.0%
; 24
 
6.0%
n 20
 
5.0%
T 20
 
5.0%
o 19
 
4.8%
c 18
 
4.5%
s 16
 
4.0%
t 15
 
3.8%
Other values (27) 179
45.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 272
68.3%
Uppercase Letter 81
 
20.4%
Other Punctuation 34
 
8.5%
Space Separator 7
 
1.8%
Dash Punctuation 4
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 32
11.8%
a 31
11.4%
e 24
 
8.8%
n 20
 
7.4%
o 19
 
7.0%
c 18
 
6.6%
s 16
 
5.9%
t 15
 
5.5%
l 13
 
4.8%
i 13
 
4.8%
Other values (11) 71
26.1%
Uppercase Letter
ValueCountFrequency (%)
T 20
24.7%
P 15
18.5%
N 13
16.0%
F 10
12.3%
E 7
 
8.6%
K 5
 
6.2%
S 5
 
6.2%
H 2
 
2.5%
A 2
 
2.5%
X 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
; 24
70.6%
. 6
 
17.6%
/ 4
 
11.8%
Space Separator
ValueCountFrequency (%)
7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 353
88.7%
Common 45
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 32
 
9.1%
a 31
 
8.8%
e 24
 
6.8%
n 20
 
5.7%
T 20
 
5.7%
o 19
 
5.4%
c 18
 
5.1%
s 16
 
4.5%
t 15
 
4.2%
P 15
 
4.2%
Other values (22) 143
40.5%
Common
ValueCountFrequency (%)
; 24
53.3%
7
 
15.6%
. 6
 
13.3%
/ 4
 
8.9%
- 4
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 32
 
8.0%
a 31
 
7.8%
e 24
 
6.0%
; 24
 
6.0%
n 20
 
5.0%
T 20
 
5.0%
o 19
 
4.8%
c 18
 
4.5%
s 16
 
4.0%
t 15
 
3.8%
Other values (27) 179
45.0%

ToolsTechHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)60.0%
Missing20
Missing (%)40.0%
Memory size528.0 B
npm
Docker
Docker;npm;Yarn
Docker;Homebrew;npm;Yarn
Docker;npm
Other values (13)
14 

Length

Max length36
Median length26
Mean length15.066667
Min length3

Characters and Unicode

Total characters452
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st rownpm
2nd rowHomebrew
3rd rowHomebrew;npm
4th rownpm
5th rowDocker;npm;Terraform

Common Values

ValueCountFrequency (%)
npm 6
 
12.0%
Docker 3
 
6.0%
Docker;npm;Yarn 3
 
6.0%
Docker;Homebrew;npm;Yarn 2
 
4.0%
Docker;npm 2
 
4.0%
Docker;Homebrew;Kubernetes 2
 
4.0%
Docker;npm;Terraform 1
 
2.0%
Homebrew;npm 1
 
2.0%
Homebrew 1
 
2.0%
Docker;Kubernetes 1
 
2.0%
Other values (8) 8
 
16.0%
(Missing) 20
40.0%

Length

2023-05-21T23:36:25.097918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
npm 6
20.0%
docker;npm;yarn 3
 
10.0%
docker 3
 
10.0%
docker;homebrew;npm;yarn 2
 
6.7%
docker;npm 2
 
6.7%
docker;homebrew;kubernetes 2
 
6.7%
docker;kubernetes;npm 1
 
3.3%
docker;homebrew;kubernetes;npm;yarn 1
 
3.3%
npm;yarn 1
 
3.3%
ansible;docker;terraform 1
 
3.3%
Other values (8) 8
26.7%

Most occurring characters

ValueCountFrequency (%)
e 63
13.9%
r 57
12.6%
; 42
9.3%
n 40
 
8.8%
m 34
 
7.5%
o 32
 
7.1%
p 22
 
4.9%
D 20
 
4.4%
c 20
 
4.4%
k 20
 
4.4%
Other values (14) 102
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 360
79.6%
Uppercase Letter 50
 
11.1%
Other Punctuation 42
 
9.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 63
17.5%
r 57
15.8%
n 40
11.1%
m 34
9.4%
o 32
8.9%
p 22
 
6.1%
c 20
 
5.6%
k 20
 
5.6%
b 17
 
4.7%
a 13
 
3.6%
Other values (7) 42
11.7%
Uppercase Letter
ValueCountFrequency (%)
D 20
40.0%
Y 8
 
16.0%
H 7
 
14.0%
K 7
 
14.0%
T 5
 
10.0%
A 3
 
6.0%
Other Punctuation
ValueCountFrequency (%)
; 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 410
90.7%
Common 42
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 63
15.4%
r 57
13.9%
n 40
9.8%
m 34
 
8.3%
o 32
 
7.8%
p 22
 
5.4%
D 20
 
4.9%
c 20
 
4.9%
k 20
 
4.9%
b 17
 
4.1%
Other values (13) 85
20.7%
Common
ValueCountFrequency (%)
; 42
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 452
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 63
13.9%
r 57
12.6%
; 42
9.3%
n 40
 
8.8%
m 34
 
7.5%
o 32
 
7.1%
p 22
 
4.9%
D 20
 
4.4%
c 20
 
4.4%
k 20
 
4.4%
Other values (14) 102
22.6%

ToolsTechWantToWorkWith
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)57.7%
Missing24
Missing (%)48.0%
Memory size528.0 B
Docker
Docker;Kubernetes
npm
Docker;npm;Yarn
Docker;npm
Other values (10)
11 

Length

Max length43
Median length21
Mean length15.961538
Min length3

Characters and Unicode

Total characters415
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)34.6%

Sample

1st rowDocker;Kubernetes
2nd rowHomebrew
3rd rownpm
4th rowUnity 3D;Yarn
5th rowDocker;Terraform

Common Values

ValueCountFrequency (%)
Docker 5
 
10.0%
Docker;Kubernetes 3
 
6.0%
npm 3
 
6.0%
Docker;npm;Yarn 2
 
4.0%
Docker;npm 2
 
4.0%
Ansible;Docker;Kubernetes;Terraform 2
 
4.0%
Homebrew 1
 
2.0%
Unity 3D;Yarn 1
 
2.0%
Docker;Terraform 1
 
2.0%
Docker;Homebrew;Kubernetes;Pulumi;Terraform 1
 
2.0%
Other values (5) 5
 
10.0%
(Missing) 24
48.0%

Length

2023-05-21T23:36:25.384980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
docker 5
18.5%
docker;kubernetes 3
11.1%
npm 3
11.1%
docker;npm;yarn 2
 
7.4%
docker;npm 2
 
7.4%
ansible;docker;kubernetes;terraform 2
 
7.4%
homebrew 1
 
3.7%
unity 1
 
3.7%
3d;yarn 1
 
3.7%
docker;terraform 1
 
3.7%
Other values (6) 6
22.2%

Most occurring characters

ValueCountFrequency (%)
e 64
15.4%
r 56
13.5%
; 33
 
8.0%
o 31
 
7.5%
n 27
 
6.5%
D 21
 
5.1%
c 20
 
4.8%
k 20
 
4.8%
m 20
 
4.8%
b 16
 
3.9%
Other values (20) 107
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 329
79.3%
Uppercase Letter 51
 
12.3%
Other Punctuation 33
 
8.0%
Space Separator 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 64
19.5%
r 56
17.0%
o 31
9.4%
n 27
8.2%
c 20
 
6.1%
k 20
 
6.1%
m 20
 
6.1%
b 16
 
4.9%
s 12
 
3.6%
u 11
 
3.3%
Other values (8) 52
15.8%
Uppercase Letter
ValueCountFrequency (%)
D 21
41.2%
K 9
17.6%
T 6
 
11.8%
Y 5
 
9.8%
H 4
 
7.8%
A 3
 
5.9%
U 1
 
2.0%
P 1
 
2.0%
F 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
; 33
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 380
91.6%
Common 35
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 64
16.8%
r 56
14.7%
o 31
 
8.2%
n 27
 
7.1%
D 21
 
5.5%
c 20
 
5.3%
k 20
 
5.3%
m 20
 
5.3%
b 16
 
4.2%
s 12
 
3.2%
Other values (17) 93
24.5%
Common
ValueCountFrequency (%)
; 33
94.3%
1
 
2.9%
3 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 64
15.4%
r 56
13.5%
; 33
 
8.0%
o 31
 
7.5%
n 27
 
6.5%
D 21
 
5.1%
c 20
 
4.8%
k 20
 
4.8%
m 20
 
4.8%
b 16
 
3.9%
Other values (20) 107
25.8%

NEWCollabToolsHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)78.7%
Missing3
Missing (%)6.0%
Memory size528.0 B
Visual Studio Code
Notepad++;Visual Studio;Visual Studio Code
 
3
Visual Studio
 
2
Visual Studio;Visual Studio Code
 
2
Rider;Visual Studio;Visual Studio Code
 
2
Other values (32)
32 

Length

Max length86
Median length48
Mean length35.085106
Min length9

Characters and Unicode

Total characters1649
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)68.1%

Sample

1st rowNotepad++;Visual Studio
2nd rowNotepad++;Visual Studio;Visual Studio Code
3rd rowNotepad++;Visual Studio;Visual Studio Code;Xcode
4th rowVisual Studio Code;Xcode
5th rowAtom;IntelliJ;Notepad++;PyCharm;Sublime Text;Visual Studio Code

Common Values

ValueCountFrequency (%)
Visual Studio Code 6
 
12.0%
Notepad++;Visual Studio;Visual Studio Code 3
 
6.0%
Visual Studio 2
 
4.0%
Visual Studio;Visual Studio Code 2
 
4.0%
Rider;Visual Studio;Visual Studio Code 2
 
4.0%
Notepad++;Rider;Visual Studio;Visual Studio Code 1
 
2.0%
CLion;IntelliJ;Nano;Notepad++;PyCharm;Vim;Visual Studio Code 1
 
2.0%
IPython/Jupyter;PyCharm;Vim;Visual Studio Code 1
 
2.0%
IPython/Jupyter;Nano;PyCharm;Sublime Text 1
 
2.0%
Android Studio;Eclipse;IntelliJ;Notepad++;PyCharm;Vim;Visual Studio;Visual Studio Code 1
 
2.0%
Other values (27) 27
54.0%
(Missing) 3
 
6.0%

Length

2023-05-21T23:36:25.645514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
studio 40
28.2%
code 29
20.4%
studio;visual 13
 
9.2%
visual 11
 
7.7%
notepad++;visual 6
 
4.2%
code;xcode 3
 
2.1%
android 3
 
2.1%
rider;visual 2
 
1.4%
text;visual 2
 
1.4%
ipython/jupyter;neovim;visual 1
 
0.7%
Other values (32) 32
22.5%

Most occurring characters

ValueCountFrequency (%)
i 158
 
9.6%
o 133
 
8.1%
d 121
 
7.3%
u 118
 
7.2%
t 108
 
6.5%
; 98
 
5.9%
e 96
 
5.8%
95
 
5.8%
l 93
 
5.6%
a 83
 
5.0%
Other values (32) 546
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1133
68.7%
Uppercase Letter 279
 
16.9%
Other Punctuation 104
 
6.3%
Space Separator 95
 
5.8%
Math Symbol 36
 
2.2%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 158
13.9%
o 133
11.7%
d 121
10.7%
u 118
10.4%
t 108
9.5%
e 96
8.5%
l 93
8.2%
a 83
7.3%
s 62
 
5.5%
n 32
 
2.8%
Other values (9) 129
11.4%
Uppercase Letter
ValueCountFrequency (%)
V 63
22.6%
S 61
21.9%
C 43
15.4%
N 25
 
9.0%
J 20
 
7.2%
I 20
 
7.2%
P 12
 
4.3%
E 8
 
2.9%
A 7
 
2.5%
R 6
 
2.2%
Other values (6) 14
 
5.0%
Other Punctuation
ValueCountFrequency (%)
; 98
94.2%
/ 5
 
4.8%
, 1
 
1.0%
Space Separator
ValueCountFrequency (%)
95
100.0%
Math Symbol
ValueCountFrequency (%)
+ 36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1412
85.6%
Common 237
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 158
11.2%
o 133
 
9.4%
d 121
 
8.6%
u 118
 
8.4%
t 108
 
7.6%
e 96
 
6.8%
l 93
 
6.6%
a 83
 
5.9%
V 63
 
4.5%
s 62
 
4.4%
Other values (25) 377
26.7%
Common
ValueCountFrequency (%)
; 98
41.4%
95
40.1%
+ 36
 
15.2%
/ 5
 
2.1%
( 1
 
0.4%
, 1
 
0.4%
) 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 158
 
9.6%
o 133
 
8.1%
d 121
 
7.3%
u 118
 
7.2%
t 108
 
6.5%
; 98
 
5.9%
e 96
 
5.8%
95
 
5.8%
l 93
 
5.6%
a 83
 
5.0%
Other values (32) 546
33.1%

NEWCollabToolsWantToWorkWith
Categorical

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)73.2%
Missing9
Missing (%)18.0%
Memory size528.0 B
Visual Studio Code
Notepad++;Visual Studio;Visual Studio Code
 
2
Visual Studio;Visual Studio Code
 
2
IPython/Jupyter;PyCharm;Vim
 
2
IntelliJ;PyCharm;Vim
 
1
Other values (25)
25 

Length

Max length72
Median length41
Mean length28.170732
Min length3

Characters and Unicode

Total characters1155
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)63.4%

Sample

1st rowNotepad++;Visual Studio
2nd rowNotepad++;Visual Studio;Visual Studio Code
3rd rowRider;Visual Studio;Visual Studio Code
4th rowVisual Studio Code
5th rowVisual Studio Code;Webstorm

Common Values

ValueCountFrequency (%)
Visual Studio Code 9
18.0%
Notepad++;Visual Studio;Visual Studio Code 2
 
4.0%
Visual Studio;Visual Studio Code 2
 
4.0%
IPython/Jupyter;PyCharm;Vim 2
 
4.0%
IntelliJ;PyCharm;Vim 1
 
2.0%
Atom;IntelliJ;Vim 1
 
2.0%
Nano;PyCharm 1
 
2.0%
IPython/Jupyter;Nano;PyCharm;Sublime Text 1
 
2.0%
IntelliJ;Vim 1
 
2.0%
IntelliJ;PhpStorm;PyCharm;RubyMine;Webstorm 1
 
2.0%
Other values (20) 20
40.0%
(Missing) 9
18.0%

Length

2023-05-21T23:36:25.932847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
studio 29
26.1%
code 22
19.8%
visual 13
11.7%
studio;visual 5
 
4.5%
android 4
 
3.6%
notepad++;visual 3
 
2.7%
ipython/jupyter;pycharm;vim 2
 
1.8%
code;webstorm 2
 
1.8%
rider;visual 2
 
1.8%
vim;visual 2
 
1.8%
Other values (27) 27
24.3%

Most occurring characters

ValueCountFrequency (%)
i 109
 
9.4%
o 91
 
7.9%
d 85
 
7.4%
u 83
 
7.2%
t 73
 
6.3%
70
 
6.1%
e 66
 
5.7%
; 59
 
5.1%
l 58
 
5.0%
a 53
 
4.6%
Other values (32) 408
35.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 801
69.4%
Uppercase Letter 201
 
17.4%
Space Separator 70
 
6.1%
Other Punctuation 65
 
5.6%
Math Symbol 16
 
1.4%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 109
13.6%
o 91
11.4%
d 85
10.6%
u 83
10.4%
t 73
9.1%
e 66
8.2%
l 58
7.2%
a 53
6.6%
s 40
 
5.0%
m 31
 
3.9%
Other values (9) 112
14.0%
Uppercase Letter
ValueCountFrequency (%)
V 45
22.4%
S 43
21.4%
C 34
16.9%
I 14
 
7.0%
J 14
 
7.0%
P 14
 
7.0%
N 11
 
5.5%
A 6
 
3.0%
R 5
 
2.5%
W 3
 
1.5%
Other values (6) 12
 
6.0%
Other Punctuation
ValueCountFrequency (%)
; 59
90.8%
/ 5
 
7.7%
, 1
 
1.5%
Space Separator
ValueCountFrequency (%)
70
100.0%
Math Symbol
ValueCountFrequency (%)
+ 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1002
86.8%
Common 153
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 109
 
10.9%
o 91
 
9.1%
d 85
 
8.5%
u 83
 
8.3%
t 73
 
7.3%
e 66
 
6.6%
l 58
 
5.8%
a 53
 
5.3%
V 45
 
4.5%
S 43
 
4.3%
Other values (25) 296
29.5%
Common
ValueCountFrequency (%)
70
45.8%
; 59
38.6%
+ 16
 
10.5%
/ 5
 
3.3%
( 1
 
0.7%
, 1
 
0.7%
) 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 109
 
9.4%
o 91
 
7.9%
d 85
 
7.4%
u 83
 
7.2%
t 73
 
6.3%
70
 
6.1%
e 66
 
5.7%
; 59
 
5.1%
l 58
 
5.0%
a 53
 
4.6%
Other values (32) 408
35.3%

OpSysProfessional use
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)20.5%
Missing6
Missing (%)12.0%
Memory size528.0 B
Windows
14 
macOS
10 
Linux-based
Linux-based;macOS
Windows;Windows Subsystem for Linux (WSL)
Other values (4)

Length

Max length41
Median length33
Mean length12.363636
Min length5

Characters and Unicode

Total characters544
Distinct characters26
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st rowmacOS
2nd rowWindows
3rd rowWindows
4th rowWindows
5th rowLinux-based;macOS

Common Values

ValueCountFrequency (%)
Windows 14
28.0%
macOS 10
20.0%
Linux-based 8
16.0%
Linux-based;macOS 3
 
6.0%
Windows;Windows Subsystem for Linux (WSL) 3
 
6.0%
Linux-based;macOS;Windows 2
 
4.0%
Linux-based;Windows 2
 
4.0%
Windows Subsystem for Linux (WSL) 1
 
2.0%
macOS;Windows 1
 
2.0%
(Missing) 6
12.0%

Length

2023-05-21T23:36:26.189509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:26.501994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
windows 15
25.0%
macos 10
16.7%
linux-based 8
13.3%
subsystem 4
 
6.7%
for 4
 
6.7%
linux 4
 
6.7%
wsl 4
 
6.7%
linux-based;macos 3
 
5.0%
windows;windows 3
 
5.0%
linux-based;macos;windows 2
 
3.3%
Other values (2) 3
 
5.0%

Most occurring characters

ValueCountFrequency (%)
s 49
 
9.0%
n 45
 
8.3%
i 45
 
8.3%
d 41
 
7.5%
a 31
 
5.7%
W 30
 
5.5%
o 30
 
5.5%
w 26
 
4.8%
S 24
 
4.4%
L 23
 
4.2%
Other values (16) 200
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 399
73.3%
Uppercase Letter 93
 
17.1%
Space Separator 16
 
2.9%
Dash Punctuation 15
 
2.8%
Other Punctuation 13
 
2.4%
Open Punctuation 4
 
0.7%
Close Punctuation 4
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 49
12.3%
n 45
11.3%
i 45
11.3%
d 41
10.3%
a 31
7.8%
o 30
7.5%
w 26
 
6.5%
u 23
 
5.8%
m 20
 
5.0%
x 19
 
4.8%
Other values (7) 70
17.5%
Uppercase Letter
ValueCountFrequency (%)
W 30
32.3%
S 24
25.8%
L 23
24.7%
O 16
17.2%
Space Separator
ValueCountFrequency (%)
16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Other Punctuation
ValueCountFrequency (%)
; 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 492
90.4%
Common 52
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 49
 
10.0%
n 45
 
9.1%
i 45
 
9.1%
d 41
 
8.3%
a 31
 
6.3%
W 30
 
6.1%
o 30
 
6.1%
w 26
 
5.3%
S 24
 
4.9%
L 23
 
4.7%
Other values (11) 148
30.1%
Common
ValueCountFrequency (%)
16
30.8%
- 15
28.8%
; 13
25.0%
( 4
 
7.7%
) 4
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 49
 
9.0%
n 45
 
8.3%
i 45
 
8.3%
d 41
 
7.5%
a 31
 
5.7%
W 30
 
5.5%
o 30
 
5.5%
w 26
 
4.8%
S 24
 
4.4%
L 23
 
4.2%
Other values (16) 200
36.8%

OpSysPersonal use
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)20.8%
Missing2
Missing (%)4.0%
Memory size528.0 B
Windows
18 
macOS
Linux-based
macOS;Windows
Linux-based;Windows
Other values (5)

Length

Max length53
Median length41
Mean length11.458333
Min length5

Characters and Unicode

Total characters550
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)8.3%

Sample

1st rowWindows Subsystem for Linux (WSL)
2nd rowWindows
3rd rowWindows
4th rowmacOS;Windows
5th rowmacOS

Common Values

ValueCountFrequency (%)
Windows 18
36.0%
macOS 9
18.0%
Linux-based 9
18.0%
macOS;Windows 3
 
6.0%
Linux-based;Windows 3
 
6.0%
Linux-based;macOS 2
 
4.0%
Windows Subsystem for Linux (WSL) 1
 
2.0%
Windows;Windows Subsystem for Linux (WSL) 1
 
2.0%
Other (please specify): 1
 
2.0%
Linux-based;Windows;Windows Subsystem for Linux (WSL) 1
 
2.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:26.778241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:27.055851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
windows 19
30.6%
macos 9
14.5%
linux-based 9
14.5%
macos;windows 3
 
4.8%
linux-based;windows 3
 
4.8%
subsystem 3
 
4.8%
for 3
 
4.8%
linux 3
 
4.8%
wsl 3
 
4.8%
linux-based;macos 2
 
3.2%
Other values (5) 5
 
8.1%

Most occurring characters

ValueCountFrequency (%)
s 52
 
9.5%
i 48
 
8.7%
n 47
 
8.5%
d 44
 
8.0%
W 32
 
5.8%
o 32
 
5.8%
a 30
 
5.5%
w 29
 
5.3%
e 22
 
4.0%
L 21
 
3.8%
Other values (20) 193
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 413
75.1%
Uppercase Letter 88
 
16.0%
Dash Punctuation 15
 
2.7%
Space Separator 14
 
2.5%
Other Punctuation 12
 
2.2%
Open Punctuation 4
 
0.7%
Close Punctuation 4
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 52
12.6%
i 48
11.6%
n 47
11.4%
d 44
10.7%
o 32
7.7%
a 30
7.3%
w 29
7.0%
e 22
 
5.3%
u 21
 
5.1%
b 18
 
4.4%
Other values (10) 70
16.9%
Uppercase Letter
ValueCountFrequency (%)
W 32
36.4%
L 21
23.9%
S 20
22.7%
O 15
17.0%
Other Punctuation
ValueCountFrequency (%)
; 11
91.7%
: 1
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 501
91.1%
Common 49
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 52
 
10.4%
i 48
 
9.6%
n 47
 
9.4%
d 44
 
8.8%
W 32
 
6.4%
o 32
 
6.4%
a 30
 
6.0%
w 29
 
5.8%
e 22
 
4.4%
L 21
 
4.2%
Other values (14) 144
28.7%
Common
ValueCountFrequency (%)
- 15
30.6%
14
28.6%
; 11
22.4%
( 4
 
8.2%
) 4
 
8.2%
: 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 52
 
9.5%
i 48
 
8.7%
n 47
 
8.5%
d 44
 
8.0%
W 32
 
5.8%
o 32
 
5.8%
a 30
 
5.5%
w 29
 
5.3%
e 22
 
4.0%
L 21
 
3.8%
Other values (20) 193
35.1%

VersionControlSystem
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)10.4%
Missing2
Missing (%)4.0%
Memory size528.0 B
Git
43 
I don't use one
 
2
Git;Other (please specify):
 
1
Mercurial;SVN
 
1
Git;SVN
 
1

Length

Max length27
Median length3
Mean length4.2916667
Min length3

Characters and Unicode

Total characters206
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.2%

Sample

1st rowGit
2nd rowGit
3rd rowGit
4th rowGit;Other (please specify):
5th rowGit

Common Values

ValueCountFrequency (%)
Git 43
86.0%
I don't use one 2
 
4.0%
Git;Other (please specify): 1
 
2.0%
Mercurial;SVN 1
 
2.0%
Git;SVN 1
 
2.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:27.345666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:27.563077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
git 43
76.8%
i 2
 
3.6%
don't 2
 
3.6%
use 2
 
3.6%
one 2
 
3.6%
git;other 1
 
1.8%
please 1
 
1.8%
specify 1
 
1.8%
mercurial;svn 1
 
1.8%
git;svn 1
 
1.8%

Most occurring characters

ValueCountFrequency (%)
t 48
23.3%
i 47
22.8%
G 45
21.8%
e 9
 
4.4%
8
 
3.9%
o 4
 
1.9%
n 4
 
1.9%
s 4
 
1.9%
; 3
 
1.5%
r 3
 
1.5%
Other values (19) 31
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135
65.5%
Uppercase Letter 55
26.7%
Space Separator 8
 
3.9%
Other Punctuation 6
 
2.9%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 48
35.6%
i 47
34.8%
e 9
 
6.7%
o 4
 
3.0%
n 4
 
3.0%
s 4
 
3.0%
r 3
 
2.2%
u 3
 
2.2%
a 2
 
1.5%
c 2
 
1.5%
Other values (6) 9
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
G 45
81.8%
S 2
 
3.6%
V 2
 
3.6%
N 2
 
3.6%
I 2
 
3.6%
O 1
 
1.8%
M 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
; 3
50.0%
' 2
33.3%
: 1
 
16.7%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 190
92.2%
Common 16
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 48
25.3%
i 47
24.7%
G 45
23.7%
e 9
 
4.7%
o 4
 
2.1%
n 4
 
2.1%
s 4
 
2.1%
r 3
 
1.6%
u 3
 
1.6%
a 2
 
1.1%
Other values (13) 21
11.1%
Common
ValueCountFrequency (%)
8
50.0%
; 3
 
18.8%
' 2
 
12.5%
( 1
 
6.2%
) 1
 
6.2%
: 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 48
23.3%
i 47
22.8%
G 45
21.8%
e 9
 
4.4%
8
 
3.9%
o 4
 
1.9%
n 4
 
1.9%
s 4
 
1.9%
; 3
 
1.5%
r 3
 
1.5%
Other values (19) 31
15.0%

VCInteraction
Categorical

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)24.4%
Missing5
Missing (%)10.0%
Memory size528.0 B
Code editor;Command-line
12 
Command-line
11 
Code editor
Code editor;Command-line;Version control hosting service web GUI;Dedicated version control GUI application
Command-line;Dedicated version control GUI application
Other values (6)
10 

Length

Max length106
Median length94
Mean length37.888889
Min length11

Characters and Unicode

Total characters1705
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)8.9%

Sample

1st rowCode editor
2nd rowCode editor;Command-line;Version control hosting service web GUI;Dedicated version control GUI application
3rd rowCode editor
4th rowCommand-line;Version control hosting service web GUI;Dedicated version control GUI application
5th rowCode editor;Command-line

Common Values

ValueCountFrequency (%)
Code editor;Command-line 12
24.0%
Command-line 11
22.0%
Code editor 5
10.0%
Code editor;Command-line;Version control hosting service web GUI;Dedicated version control GUI application 4
 
8.0%
Command-line;Dedicated version control GUI application 3
 
6.0%
Code editor;Dedicated version control GUI application 3
 
6.0%
Code editor;Command-line;Version control hosting service web GUI 3
 
6.0%
Command-line;Version control hosting service web GUI;Dedicated version control GUI application 1
 
2.0%
Version control hosting service web GUI;Dedicated version control GUI application 1
 
2.0%
Code editor;Command-line;Dedicated version control GUI application 1
 
2.0%
(Missing) 5
10.0%

Length

2023-05-21T23:36:27.783097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
code 28
16.0%
control 23
13.1%
gui 17
9.7%
version 14
8.0%
application 13
7.4%
editor;command-line 12
6.9%
command-line 11
 
6.3%
web 10
 
5.7%
service 10
 
5.7%
hosting 10
 
5.7%
Other values (7) 27
15.4%

Most occurring characters

ValueCountFrequency (%)
o 184
 
10.8%
e 171
 
10.0%
i 146
 
8.6%
n 141
 
8.3%
130
 
7.6%
d 118
 
6.9%
t 87
 
5.1%
r 84
 
4.9%
a 75
 
4.4%
l 72
 
4.2%
Other values (17) 497
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1341
78.7%
Uppercase Letter 156
 
9.1%
Space Separator 130
 
7.6%
Other Punctuation 42
 
2.5%
Dash Punctuation 36
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 184
13.7%
e 171
12.8%
i 146
10.9%
n 141
10.5%
d 118
8.8%
t 87
6.5%
r 84
6.3%
a 75
 
5.6%
l 72
 
5.4%
m 72
 
5.4%
Other values (8) 191
14.2%
Uppercase Letter
ValueCountFrequency (%)
C 64
41.0%
G 23
 
14.7%
U 23
 
14.7%
I 23
 
14.7%
D 13
 
8.3%
V 10
 
6.4%
Space Separator
ValueCountFrequency (%)
130
100.0%
Other Punctuation
ValueCountFrequency (%)
; 42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1497
87.8%
Common 208
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 184
12.3%
e 171
11.4%
i 146
9.8%
n 141
9.4%
d 118
 
7.9%
t 87
 
5.8%
r 84
 
5.6%
a 75
 
5.0%
l 72
 
4.8%
m 72
 
4.8%
Other values (14) 347
23.2%
Common
ValueCountFrequency (%)
130
62.5%
; 42
 
20.2%
- 36
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 184
 
10.8%
e 171
 
10.0%
i 146
 
8.6%
n 141
 
8.3%
130
 
7.6%
d 118
 
6.9%
t 87
 
5.1%
r 84
 
4.9%
a 75
 
4.4%
l 72
 
4.2%
Other values (17) 497
29.1%

VCHostingPersonal use
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size528.0 B

VCHostingProfessional use
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing50
Missing (%)100.0%
Memory size528.0 B

OfficeStackAsyncHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)57.7%
Missing24
Missing (%)48.0%
Memory size528.0 B
Jira Work Management;Trello
Confluence
Confluence;Jira Work Management
Confluence;Jira Work Management;Notion;Trello
Jira Work Management
Other values (10)
10 

Length

Max length54
Median length39.5
Mean length24.846154
Min length6

Characters and Unicode

Total characters646
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)38.5%

Sample

1st rowJira Work Management;Trello
2nd rowConfluence
3rd rowConfluence;Jira Work Management
4th rowConfluence
5th rowConfluence;Jira Work Management;Notion;Trello

Common Values

ValueCountFrequency (%)
Jira Work Management;Trello 4
 
8.0%
Confluence 4
 
8.0%
Confluence;Jira Work Management 4
 
8.0%
Confluence;Jira Work Management;Notion;Trello 2
 
4.0%
Jira Work Management 2
 
4.0%
Airtable;Confluence;Jira Work Management;Notion;Trello 1
 
2.0%
Asana;Jira Work Management;Trello 1
 
2.0%
Trello 1
 
2.0%
Notion 1
 
2.0%
Confluence;Notion 1
 
2.0%
Other values (5) 5
 
10.0%
(Missing) 24
48.0%

Length

2023-05-21T23:36:28.028110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
work 17
28.3%
management;trello 7
11.7%
confluence;jira 7
11.7%
management 7
11.7%
jira 6
 
10.0%
confluence 4
 
6.7%
management;notion;trello 3
 
5.0%
asana;jira 2
 
3.3%
airtable;confluence;jira 1
 
1.7%
trello 1
 
1.7%
Other values (5) 5
 
8.3%

Most occurring characters

ValueCountFrequency (%)
e 73
 
11.3%
n 70
 
10.8%
a 59
 
9.1%
o 56
 
8.7%
r 47
 
7.3%
l 39
 
6.0%
34
 
5.3%
; 28
 
4.3%
i 25
 
3.9%
t 24
 
3.7%
Other values (20) 191
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 495
76.6%
Uppercase Letter 88
 
13.6%
Space Separator 34
 
5.3%
Other Punctuation 29
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 73
14.7%
n 70
14.1%
a 59
11.9%
o 56
11.3%
r 47
9.5%
l 39
7.9%
i 25
 
5.1%
t 24
 
4.8%
m 19
 
3.8%
k 18
 
3.6%
Other values (9) 65
13.1%
Uppercase Letter
ValueCountFrequency (%)
J 17
19.3%
M 17
19.3%
W 17
19.3%
C 14
15.9%
T 12
13.6%
N 6
 
6.8%
A 4
 
4.5%
U 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
; 28
96.6%
. 1
 
3.4%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 583
90.2%
Common 63
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 73
12.5%
n 70
12.0%
a 59
 
10.1%
o 56
 
9.6%
r 47
 
8.1%
l 39
 
6.7%
i 25
 
4.3%
t 24
 
4.1%
m 19
 
3.3%
k 18
 
3.1%
Other values (17) 153
26.2%
Common
ValueCountFrequency (%)
34
54.0%
; 28
44.4%
. 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 646
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 73
 
11.3%
n 70
 
10.8%
a 59
 
9.1%
o 56
 
8.7%
r 47
 
7.3%
l 39
 
6.0%
34
 
5.3%
; 28
 
4.3%
i 25
 
3.9%
t 24
 
3.7%
Other values (20) 191
29.6%

OfficeStackAsyncWantToWorkWith
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)55.6%
Missing32
Missing (%)64.0%
Memory size528.0 B
Jira Work Management;Trello
Jira Work Management
Confluence;Jira Work Management
Notion
Trello
Other values (5)

Length

Max length33
Median length27
Mean length17.111111
Min length5

Characters and Unicode

Total characters308
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)22.2%

Sample

1st rowJira Work Management;Trello
2nd rowConfluence;Jira Work Management
3rd rowNotion;Trello
4th rowJira Work Management;Trello
5th rowNotion

Common Values

ValueCountFrequency (%)
Jira Work Management;Trello 3
 
6.0%
Jira Work Management 3
 
6.0%
Confluence;Jira Work Management 2
 
4.0%
Notion 2
 
4.0%
Trello 2
 
4.0%
Confluence 2
 
4.0%
Notion;Trello 1
 
2.0%
Asana;Jira Work Management;Trello 1
 
2.0%
Asana 1
 
2.0%
monday.com 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:28.277559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:28.581303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
work 9
25.0%
jira 6
16.7%
management 5
13.9%
management;trello 4
11.1%
confluence;jira 2
 
5.6%
notion 2
 
5.6%
trello 2
 
5.6%
confluence 2
 
5.6%
notion;trello 1
 
2.8%
asana;jira 1
 
2.8%
Other values (2) 2
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 33
 
10.7%
a 32
 
10.4%
n 32
 
10.4%
o 28
 
9.1%
r 25
 
8.1%
18
 
5.8%
l 18
 
5.8%
t 12
 
3.9%
i 12
 
3.9%
m 11
 
3.6%
Other values (17) 87
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 238
77.3%
Uppercase Letter 43
 
14.0%
Space Separator 18
 
5.8%
Other Punctuation 9
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33
13.9%
a 32
13.4%
n 32
13.4%
o 28
11.8%
r 25
10.5%
l 18
7.6%
t 12
 
5.0%
i 12
 
5.0%
m 11
 
4.6%
g 9
 
3.8%
Other values (7) 26
10.9%
Uppercase Letter
ValueCountFrequency (%)
J 9
20.9%
M 9
20.9%
W 9
20.9%
T 7
16.3%
C 4
9.3%
N 3
 
7.0%
A 2
 
4.7%
Other Punctuation
ValueCountFrequency (%)
; 8
88.9%
. 1
 
11.1%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 281
91.2%
Common 27
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33
11.7%
a 32
11.4%
n 32
11.4%
o 28
10.0%
r 25
 
8.9%
l 18
 
6.4%
t 12
 
4.3%
i 12
 
4.3%
m 11
 
3.9%
J 9
 
3.2%
Other values (14) 69
24.6%
Common
ValueCountFrequency (%)
18
66.7%
; 8
29.6%
. 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 33
 
10.7%
a 32
 
10.4%
n 32
 
10.4%
o 28
 
9.1%
r 25
 
8.1%
18
 
5.8%
l 18
 
5.8%
t 12
 
3.9%
i 12
 
3.9%
m 11
 
3.6%
Other values (17) 87
28.2%

OfficeStackSyncHaveWorkedWith
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)35.9%
Missing11
Missing (%)22.0%
Memory size528.0 B
Microsoft Teams
Microsoft Teams;Slack;Zoom
Microsoft Teams;Zoom
Slack;Zoom
Microsoft Teams;Slack
Other values (9)
12 

Length

Max length68
Median length33
Mean length20.769231
Min length4

Characters and Unicode

Total characters810
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)15.4%

Sample

1st rowMicrosoft Teams
2nd rowSlack;Zoom
3rd rowMicrosoft Teams;Zoom
4th rowRocketchat;Slack;Zoom
5th rowGoogle Chat;Microsoft Teams;Slack;Zoom

Common Values

ValueCountFrequency (%)
Microsoft Teams 8
16.0%
Microsoft Teams;Slack;Zoom 7
14.0%
Microsoft Teams;Zoom 5
10.0%
Slack;Zoom 4
 
8.0%
Microsoft Teams;Slack 3
 
6.0%
Google Chat;Microsoft Teams;Slack;Zoom 2
 
4.0%
Google Chat;Slack;Zoom 2
 
4.0%
Slack 2
 
4.0%
Rocketchat;Slack;Zoom 1
 
2.0%
Google Chat;Microsoft Teams;Zoom 1
 
2.0%
Other values (4) 4
 
8.0%
(Missing) 11
22.0%

Length

2023-05-21T23:36:28.915289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
microsoft 23
29.9%
teams;slack;zoom 9
 
11.7%
teams 8
 
10.4%
google 7
 
9.1%
teams;zoom 6
 
7.8%
chat;microsoft 5
 
6.5%
slack;zoom 4
 
5.2%
teams;slack 4
 
5.2%
slack 2
 
2.6%
chat;slack;zoom 2
 
2.6%
Other values (7) 7
 
9.1%

Most occurring characters

ValueCountFrequency (%)
o 122
15.1%
a 63
 
7.8%
s 58
 
7.2%
c 55
 
6.8%
m 53
 
6.5%
; 48
 
5.9%
e 41
 
5.1%
t 39
 
4.8%
38
 
4.7%
l 33
 
4.1%
Other values (17) 260
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 598
73.8%
Uppercase Letter 126
 
15.6%
Other Punctuation 48
 
5.9%
Space Separator 38
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 122
20.4%
a 63
10.5%
s 58
9.7%
c 55
9.2%
m 53
8.9%
e 41
 
6.9%
t 39
 
6.5%
l 33
 
5.5%
i 30
 
5.0%
r 29
 
4.8%
Other values (7) 75
12.5%
Uppercase Letter
ValueCountFrequency (%)
T 29
23.0%
M 28
22.2%
S 24
19.0%
Z 24
19.0%
C 10
 
7.9%
G 8
 
6.3%
R 2
 
1.6%
W 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
; 48
100.0%
Space Separator
ValueCountFrequency (%)
38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 724
89.4%
Common 86
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 122
16.9%
a 63
 
8.7%
s 58
 
8.0%
c 55
 
7.6%
m 53
 
7.3%
e 41
 
5.7%
t 39
 
5.4%
l 33
 
4.6%
i 30
 
4.1%
r 29
 
4.0%
Other values (15) 201
27.8%
Common
ValueCountFrequency (%)
; 48
55.8%
38
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 122
15.1%
a 63
 
7.8%
s 58
 
7.2%
c 55
 
6.8%
m 53
 
6.5%
; 48
 
5.9%
e 41
 
5.1%
t 39
 
4.8%
38
 
4.7%
l 33
 
4.1%
Other values (17) 260
32.1%

OfficeStackSyncWantToWorkWith
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)32.3%
Missing19
Missing (%)38.0%
Memory size528.0 B
Microsoft Teams
Slack
Slack;Zoom
Microsoft Teams;Slack
Microsoft Teams;Zoom
Other values (5)

Length

Max length22
Median length20
Mean length13.258065
Min length4

Characters and Unicode

Total characters411
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)12.9%

Sample

1st rowMicrosoft Teams
2nd rowSlack;Zoom
3rd rowRocketchat;Slack;Zoom
4th rowGoogle Chat;Slack;Zoom
5th rowMicrosoft Teams

Common Values

ValueCountFrequency (%)
Microsoft Teams 7
 
14.0%
Slack 7
 
14.0%
Slack;Zoom 5
 
10.0%
Microsoft Teams;Slack 3
 
6.0%
Microsoft Teams;Zoom 3
 
6.0%
Google Chat;Slack 2
 
4.0%
Rocketchat;Slack;Zoom 1
 
2.0%
Google Chat;Slack;Zoom 1
 
2.0%
Zoom 1
 
2.0%
RingCentral;Slack 1
 
2.0%
(Missing) 19
38.0%

Length

2023-05-21T23:36:29.162921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:29.563114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
microsoft 13
27.7%
teams 7
14.9%
slack 7
14.9%
slack;zoom 5
 
10.6%
teams;slack 3
 
6.4%
teams;zoom 3
 
6.4%
google 3
 
6.4%
chat;slack 2
 
4.3%
rocketchat;slack;zoom 1
 
2.1%
chat;slack;zoom 1
 
2.1%
Other values (2) 2
 
4.3%

Most occurring characters

ValueCountFrequency (%)
o 55
13.4%
a 38
 
9.2%
c 35
 
8.5%
s 26
 
6.3%
m 24
 
5.8%
l 24
 
5.8%
k 21
 
5.1%
S 20
 
4.9%
t 19
 
4.6%
; 18
 
4.4%
Other values (14) 131
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 311
75.7%
Uppercase Letter 66
 
16.1%
Other Punctuation 18
 
4.4%
Space Separator 16
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 55
17.7%
a 38
12.2%
c 35
11.3%
s 26
8.4%
m 24
7.7%
l 24
7.7%
k 21
 
6.8%
t 19
 
6.1%
e 18
 
5.8%
r 14
 
4.5%
Other values (5) 37
11.9%
Uppercase Letter
ValueCountFrequency (%)
S 20
30.3%
M 13
19.7%
T 13
19.7%
Z 11
16.7%
C 4
 
6.1%
G 3
 
4.5%
R 2
 
3.0%
Other Punctuation
ValueCountFrequency (%)
; 18
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 377
91.7%
Common 34
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 55
14.6%
a 38
 
10.1%
c 35
 
9.3%
s 26
 
6.9%
m 24
 
6.4%
l 24
 
6.4%
k 21
 
5.6%
S 20
 
5.3%
t 19
 
5.0%
e 18
 
4.8%
Other values (12) 97
25.7%
Common
ValueCountFrequency (%)
; 18
52.9%
16
47.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 411
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 55
13.4%
a 38
 
9.2%
c 35
 
8.5%
s 26
 
6.3%
m 24
 
5.8%
l 24
 
5.8%
k 21
 
5.1%
S 20
 
4.9%
t 19
 
4.6%
; 18
 
4.4%
Other values (14) 131
31.9%

Blockchain
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)12.8%
Missing3
Missing (%)6.0%
Memory size528.0 B
Very unfavorable
16 
Indifferent
10 
Unfavorable
Favorable
Unsure

Length

Max length16
Median length14
Mean length12.212766
Min length6

Characters and Unicode

Total characters574
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery unfavorable
2nd rowVery unfavorable
3rd rowVery unfavorable
4th rowUnfavorable
5th rowVery unfavorable

Common Values

ValueCountFrequency (%)
Very unfavorable 16
32.0%
Indifferent 10
20.0%
Unfavorable 7
14.0%
Favorable 5
 
10.0%
Unsure 5
 
10.0%
Very favorable 4
 
8.0%
(Missing) 3
 
6.0%

Length

2023-05-21T23:36:30.030259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:30.362826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
unfavorable 23
34.3%
very 20
29.9%
indifferent 10
14.9%
favorable 9
 
13.4%
unsure 5
 
7.5%

Most occurring characters

ValueCountFrequency (%)
e 77
13.4%
r 67
11.7%
a 64
11.1%
n 48
 
8.4%
f 47
 
8.2%
o 32
 
5.6%
l 32
 
5.6%
b 32
 
5.6%
v 32
 
5.6%
u 21
 
3.7%
Other values (10) 122
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 507
88.3%
Uppercase Letter 47
 
8.2%
Space Separator 20
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 77
15.2%
r 67
13.2%
a 64
12.6%
n 48
9.5%
f 47
9.3%
o 32
6.3%
l 32
6.3%
b 32
6.3%
v 32
6.3%
u 21
 
4.1%
Other values (5) 55
10.8%
Uppercase Letter
ValueCountFrequency (%)
V 20
42.6%
U 12
25.5%
I 10
21.3%
F 5
 
10.6%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 554
96.5%
Common 20
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 77
13.9%
r 67
12.1%
a 64
11.6%
n 48
8.7%
f 47
8.5%
o 32
 
5.8%
l 32
 
5.8%
b 32
 
5.8%
v 32
 
5.8%
u 21
 
3.8%
Other values (9) 102
18.4%
Common
ValueCountFrequency (%)
20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 77
13.4%
r 67
11.7%
a 64
11.1%
n 48
 
8.4%
f 47
 
8.2%
o 32
 
5.6%
l 32
 
5.6%
b 32
 
5.6%
v 32
 
5.6%
u 21
 
3.7%
Other values (10) 122
21.3%

NEWSOSites
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)10.4%
Missing2
Missing (%)4.0%
Memory size528.0 B
Stack Overflow;Stack Exchange
29 
Stack Overflow
Collectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack Exchange
Collectives on Stack Overflow;Stack Overflow;Stack Exchange
Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack Exchange
 
2

Length

Max length151
Median length29
Mean length46.479167
Min length14

Characters and Unicode

Total characters2231
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack Exchange
2nd rowCollectives on Stack Overflow;Stack Overflow;Stack Exchange
3rd rowCollectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack Exchange
4th rowCollectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack Exchange
5th rowStack Overflow;Stack Exchange

Common Values

ValueCountFrequency (%)
Stack Overflow;Stack Exchange 29
58.0%
Stack Overflow 7
 
14.0%
Collectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack Exchange 5
 
10.0%
Collectives on Stack Overflow;Stack Overflow;Stack Exchange 5
 
10.0%
Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack Exchange 2
 
4.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:30.932775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:31.397845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
overflow;stack 51
20.9%
stack 48
19.7%
exchange 41
16.8%
overflow 14
 
5.7%
for 14
 
5.7%
collectives 10
 
4.1%
on 10
 
4.1%
teams 7
 
2.9%
private 7
 
2.9%
knowledge 7
 
2.9%
Other values (5) 35
14.3%

Most occurring characters

ValueCountFrequency (%)
a 196
 
8.8%
196
 
8.8%
c 171
 
7.7%
e 161
 
7.2%
o 141
 
6.3%
t 137
 
6.1%
l 113
 
5.1%
k 113
 
5.1%
r 107
 
4.8%
S 106
 
4.8%
Other values (21) 790
35.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1727
77.4%
Uppercase Letter 229
 
10.3%
Space Separator 196
 
8.8%
Other Punctuation 65
 
2.9%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 196
11.3%
c 171
9.9%
e 161
 
9.3%
o 141
 
8.2%
t 137
 
7.9%
l 113
 
6.5%
k 113
 
6.5%
r 107
 
6.2%
f 86
 
5.0%
v 82
 
4.7%
Other values (11) 420
24.3%
Uppercase Letter
ValueCountFrequency (%)
S 106
46.3%
O 65
28.4%
E 41
 
17.9%
C 10
 
4.4%
T 7
 
3.1%
Other Punctuation
ValueCountFrequency (%)
; 58
89.2%
& 7
 
10.8%
Space Separator
ValueCountFrequency (%)
196
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1956
87.7%
Common 275
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 196
 
10.0%
c 171
 
8.7%
e 161
 
8.2%
o 141
 
7.2%
t 137
 
7.0%
l 113
 
5.8%
k 113
 
5.8%
r 107
 
5.5%
S 106
 
5.4%
f 86
 
4.4%
Other values (16) 625
32.0%
Common
ValueCountFrequency (%)
196
71.3%
; 58
 
21.1%
( 7
 
2.5%
& 7
 
2.5%
) 7
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2231
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 196
 
8.8%
196
 
8.8%
c 171
 
7.7%
e 161
 
7.2%
o 141
 
6.3%
t 137
 
6.1%
l 113
 
5.1%
k 113
 
5.1%
r 107
 
4.8%
S 106
 
4.8%
Other values (21) 790
35.4%

SOVisitFreq
Categorical

Distinct5
Distinct (%)10.4%
Missing2
Missing (%)4.0%
Memory size528.0 B
Daily or almost daily
19 
Multiple times per day
17 
A few times per month or weekly
A few times per week
Less than once per month or monthly
 
1

Length

Max length35
Median length31
Mean length23.020833
Min length20

Characters and Unicode

Total characters1105
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st rowDaily or almost daily
2nd rowMultiple times per day
3rd rowDaily or almost daily
4th rowMultiple times per day
5th rowMultiple times per day

Common Values

ValueCountFrequency (%)
Daily or almost daily 19
38.0%
Multiple times per day 17
34.0%
A few times per month or weekly 7
 
14.0%
A few times per week 4
 
8.0%
Less than once per month or monthly 1
 
2.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:31.909787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:32.432768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
daily 38
17.3%
per 29
13.2%
times 28
12.7%
or 27
12.3%
almost 19
8.6%
multiple 17
7.7%
day 17
7.7%
a 11
 
5.0%
few 11
 
5.0%
month 8
 
3.6%
Other values (6) 15
 
6.8%

Most occurring characters

ValueCountFrequency (%)
172
15.6%
e 109
9.9%
l 99
 
9.0%
i 83
 
7.5%
a 75
 
6.8%
t 74
 
6.7%
y 63
 
5.7%
o 56
 
5.1%
r 56
 
5.1%
m 56
 
5.1%
Other values (14) 262
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 885
80.1%
Space Separator 172
 
15.6%
Uppercase Letter 48
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 109
12.3%
l 99
11.2%
i 83
9.4%
a 75
8.5%
t 74
8.4%
y 63
 
7.1%
o 56
 
6.3%
r 56
 
6.3%
m 56
 
6.3%
s 49
 
5.5%
Other values (9) 165
18.6%
Uppercase Letter
ValueCountFrequency (%)
D 19
39.6%
M 17
35.4%
A 11
22.9%
L 1
 
2.1%
Space Separator
ValueCountFrequency (%)
172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 933
84.4%
Common 172
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 109
11.7%
l 99
10.6%
i 83
 
8.9%
a 75
 
8.0%
t 74
 
7.9%
y 63
 
6.8%
o 56
 
6.0%
r 56
 
6.0%
m 56
 
6.0%
s 49
 
5.3%
Other values (13) 213
22.8%
Common
ValueCountFrequency (%)
172
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
172
15.6%
e 109
9.9%
l 99
 
9.0%
i 83
 
7.5%
a 75
 
6.8%
t 74
 
6.7%
y 63
 
5.7%
o 56
 
5.1%
r 56
 
5.1%
m 56
 
5.1%
Other values (14) 262
23.7%

SOAccount
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)6.2%
Missing2
Missing (%)4.0%
Memory size528.0 B
Yes
40 
No
Not sure/can't remember
 
3

Length

Max length23
Median length3
Mean length4.1458333
Min length2

Characters and Unicode

Total characters199
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 40
80.0%
No 5
 
10.0%
Not sure/can't remember 3
 
6.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:32.767728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:33.062653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
yes 40
74.1%
no 5
 
9.3%
not 3
 
5.6%
sure/can't 3
 
5.6%
remember 3
 
5.6%

Most occurring characters

ValueCountFrequency (%)
e 52
26.1%
s 43
21.6%
Y 40
20.1%
r 9
 
4.5%
N 8
 
4.0%
o 8
 
4.0%
t 6
 
3.0%
6
 
3.0%
m 6
 
3.0%
u 3
 
1.5%
Other values (6) 18
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139
69.8%
Uppercase Letter 48
 
24.1%
Space Separator 6
 
3.0%
Other Punctuation 6
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 52
37.4%
s 43
30.9%
r 9
 
6.5%
o 8
 
5.8%
t 6
 
4.3%
m 6
 
4.3%
u 3
 
2.2%
c 3
 
2.2%
a 3
 
2.2%
n 3
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
Y 40
83.3%
N 8
 
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 3
50.0%
' 3
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 187
94.0%
Common 12
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 52
27.8%
s 43
23.0%
Y 40
21.4%
r 9
 
4.8%
N 8
 
4.3%
o 8
 
4.3%
t 6
 
3.2%
m 6
 
3.2%
u 3
 
1.6%
c 3
 
1.6%
Other values (3) 9
 
4.8%
Common
ValueCountFrequency (%)
6
50.0%
/ 3
25.0%
' 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 52
26.1%
s 43
21.6%
Y 40
20.1%
r 9
 
4.5%
N 8
 
4.0%
o 8
 
4.0%
t 6
 
3.0%
6
 
3.0%
m 6
 
3.0%
u 3
 
1.5%
Other values (6) 18
 
9.0%

SOPartFreq
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)15.0%
Missing10
Missing (%)20.0%
Memory size528.0 B
Less than once per month or monthly
11 
Multiple times per day
I have never participated in Q&A on Stack Overflow
A few times per month or weekly
Daily or almost daily

Length

Max length50
Median length31
Mean length31.775
Min length20

Characters and Unicode

Total characters1271
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDaily or almost daily
2nd rowMultiple times per day
3rd rowA few times per week
4th rowDaily or almost daily
5th rowMultiple times per day

Common Values

ValueCountFrequency (%)
Less than once per month or monthly 11
22.0%
Multiple times per day 7
14.0%
I have never participated in Q&A on Stack Overflow 7
14.0%
A few times per month or weekly 7
14.0%
Daily or almost daily 5
10.0%
A few times per week 3
 
6.0%
(Missing) 10
20.0%

Length

2023-05-21T23:36:33.316915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:33.713987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
per 28
 
11.1%
or 23
 
9.1%
month 18
 
7.1%
times 17
 
6.7%
less 11
 
4.4%
once 11
 
4.4%
monthly 11
 
4.4%
than 11
 
4.4%
daily 10
 
4.0%
few 10
 
4.0%
Other values (15) 102
40.5%

Most occurring characters

ValueCountFrequency (%)
212
16.7%
e 139
 
10.9%
t 90
 
7.1%
o 82
 
6.5%
n 72
 
5.7%
r 72
 
5.7%
a 61
 
4.8%
i 55
 
4.3%
l 54
 
4.2%
m 51
 
4.0%
Other values (20) 383
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 984
77.4%
Space Separator 212
 
16.7%
Uppercase Letter 68
 
5.4%
Other Punctuation 7
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 139
14.1%
t 90
 
9.1%
o 82
 
8.3%
n 72
 
7.3%
r 72
 
7.3%
a 61
 
6.2%
i 55
 
5.6%
l 54
 
5.5%
m 51
 
5.2%
p 49
 
5.0%
Other values (10) 259
26.3%
Uppercase Letter
ValueCountFrequency (%)
A 17
25.0%
L 11
16.2%
O 7
10.3%
S 7
10.3%
M 7
10.3%
Q 7
10.3%
I 7
10.3%
D 5
 
7.4%
Space Separator
ValueCountFrequency (%)
212
100.0%
Other Punctuation
ValueCountFrequency (%)
& 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1052
82.8%
Common 219
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 139
13.2%
t 90
 
8.6%
o 82
 
7.8%
n 72
 
6.8%
r 72
 
6.8%
a 61
 
5.8%
i 55
 
5.2%
l 54
 
5.1%
m 51
 
4.8%
p 49
 
4.7%
Other values (18) 327
31.1%
Common
ValueCountFrequency (%)
212
96.8%
& 7
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
212
16.7%
e 139
 
10.9%
t 90
 
7.1%
o 82
 
6.5%
n 72
 
5.7%
r 72
 
5.7%
a 61
 
4.8%
i 55
 
4.3%
l 54
 
4.2%
m 51
 
4.0%
Other values (20) 383
30.1%

SOComm
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)12.5%
Missing2
Missing (%)4.0%
Memory size528.0 B
Yes, definitely
15 
No, not really
12 
Neutral
Yes, somewhat
No, not at all

Length

Max length15
Median length14
Mean length12.854167
Min length7

Characters and Unicode

Total characters617
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st rowNot sure
2nd rowNeutral
3rd rowYes, definitely
4th rowYes, definitely
5th rowYes, definitely

Common Values

ValueCountFrequency (%)
Yes, definitely 15
30.0%
No, not really 12
24.0%
Neutral 8
16.0%
Yes, somewhat 8
16.0%
No, not at all 4
 
8.0%
Not sure 1
 
2.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:34.002786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:34.286349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
yes 23
21.3%
not 17
15.7%
no 16
14.8%
definitely 15
13.9%
really 12
11.1%
neutral 8
 
7.4%
somewhat 8
 
7.4%
at 4
 
3.7%
all 4
 
3.7%
sure 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 82
13.3%
60
 
9.7%
l 55
 
8.9%
t 52
 
8.4%
o 41
 
6.6%
, 39
 
6.3%
a 36
 
5.8%
s 32
 
5.2%
n 31
 
5.0%
i 30
 
4.9%
Other values (10) 159
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 470
76.2%
Space Separator 60
 
9.7%
Uppercase Letter 48
 
7.8%
Other Punctuation 39
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 82
17.4%
l 55
11.7%
t 52
11.1%
o 41
8.7%
a 36
7.7%
s 32
 
6.8%
n 31
 
6.6%
i 30
 
6.4%
y 27
 
5.7%
r 21
 
4.5%
Other values (6) 63
13.4%
Uppercase Letter
ValueCountFrequency (%)
N 25
52.1%
Y 23
47.9%
Space Separator
ValueCountFrequency (%)
60
100.0%
Other Punctuation
ValueCountFrequency (%)
, 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 518
84.0%
Common 99
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 82
15.8%
l 55
10.6%
t 52
10.0%
o 41
 
7.9%
a 36
 
6.9%
s 32
 
6.2%
n 31
 
6.0%
i 30
 
5.8%
y 27
 
5.2%
N 25
 
4.8%
Other values (8) 107
20.7%
Common
ValueCountFrequency (%)
60
60.6%
, 39
39.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 82
13.3%
60
 
9.7%
l 55
 
8.9%
t 52
 
8.4%
o 41
 
6.6%
, 39
 
6.3%
a 36
 
5.8%
s 32
 
5.2%
n 31
 
5.0%
i 30
 
4.9%
Other values (10) 159
25.8%

Age
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)13.0%
Missing4
Missing (%)8.0%
Memory size528.0 B
25-34 years old
21 
35-44 years old
18-24 years old
45-54 years old
55-64 years old

Length

Max length18
Median length15
Mean length15.130435
Min length15

Characters and Unicode

Total characters696
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25-34 years old
2nd row35-44 years old
3rd row25-34 years old
4th row25-34 years old
5th rowUnder 18 years old

Common Values

ValueCountFrequency (%)
25-34 years old 21
42.0%
35-44 years old 9
18.0%
18-24 years old 8
 
16.0%
45-54 years old 3
 
6.0%
55-64 years old 3
 
6.0%
Under 18 years old 2
 
4.0%
(Missing) 4
 
8.0%

Length

2023-05-21T23:36:34.566395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:34.902769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
years 46
32.9%
old 46
32.9%
25-34 21
15.0%
35-44 9
 
6.4%
18-24 8
 
5.7%
45-54 3
 
2.1%
55-64 3
 
2.1%
under 2
 
1.4%
18 2
 
1.4%

Most occurring characters

ValueCountFrequency (%)
94
13.5%
4 56
 
8.0%
r 48
 
6.9%
d 48
 
6.9%
e 48
 
6.9%
y 46
 
6.6%
a 46
 
6.6%
s 46
 
6.6%
o 46
 
6.6%
l 46
 
6.6%
Other values (9) 172
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 376
54.0%
Decimal Number 180
25.9%
Space Separator 94
 
13.5%
Dash Punctuation 44
 
6.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 48
12.8%
d 48
12.8%
e 48
12.8%
y 46
12.2%
a 46
12.2%
s 46
12.2%
o 46
12.2%
l 46
12.2%
n 2
 
0.5%
Decimal Number
ValueCountFrequency (%)
4 56
31.1%
5 42
23.3%
3 30
16.7%
2 29
16.1%
1 10
 
5.6%
8 10
 
5.6%
6 3
 
1.7%
Space Separator
ValueCountFrequency (%)
94
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378
54.3%
Common 318
45.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 48
12.7%
d 48
12.7%
e 48
12.7%
y 46
12.2%
a 46
12.2%
s 46
12.2%
o 46
12.2%
l 46
12.2%
U 2
 
0.5%
n 2
 
0.5%
Common
ValueCountFrequency (%)
94
29.6%
4 56
17.6%
- 44
13.8%
5 42
13.2%
3 30
 
9.4%
2 29
 
9.1%
1 10
 
3.1%
8 10
 
3.1%
6 3
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
94
13.5%
4 56
 
8.0%
r 48
 
6.9%
d 48
 
6.9%
e 48
 
6.9%
y 46
 
6.6%
a 46
 
6.6%
s 46
 
6.6%
o 46
 
6.6%
l 46
 
6.6%
Other values (9) 172
24.7%

Gender
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)10.9%
Missing4
Missing (%)8.0%
Memory size528.0 B
Man
37 
Woman
Or, in your own words:
 
1
Non-binary, genderqueer, or gender non-conforming
 
1
Prefer not to say
 
1

Length

Max length49
Median length3
Mean length4.9782609
Min length3

Characters and Unicode

Total characters229
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.5%

Sample

1st rowMan
2nd rowMan
3rd rowOr, in your own words:
4th rowMan
5th rowMan

Common Values

ValueCountFrequency (%)
Man 37
74.0%
Woman 6
 
12.0%
Or, in your own words: 1
 
2.0%
Non-binary, genderqueer, or gender non-conforming 1
 
2.0%
Prefer not to say 1
 
2.0%
(Missing) 4
 
8.0%

Length

2023-05-21T23:36:35.141453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:35.376147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
man 37
64.9%
woman 6
 
10.5%
or 2
 
3.5%
in 1
 
1.8%
your 1
 
1.8%
own 1
 
1.8%
words 1
 
1.8%
non-binary 1
 
1.8%
genderqueer 1
 
1.8%
gender 1
 
1.8%
Other values (5) 5
 
8.8%

Most occurring characters

ValueCountFrequency (%)
n 54
23.6%
a 45
19.7%
M 37
16.2%
o 16
 
7.0%
r 11
 
4.8%
11
 
4.8%
e 8
 
3.5%
m 7
 
3.1%
W 6
 
2.6%
, 3
 
1.3%
Other values (17) 31
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 166
72.5%
Uppercase Letter 46
 
20.1%
Space Separator 11
 
4.8%
Other Punctuation 4
 
1.7%
Dash Punctuation 2
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 54
32.5%
a 45
27.1%
o 16
 
9.6%
r 11
 
6.6%
e 8
 
4.8%
m 7
 
4.2%
i 3
 
1.8%
y 3
 
1.8%
d 3
 
1.8%
g 3
 
1.8%
Other values (8) 13
 
7.8%
Uppercase Letter
ValueCountFrequency (%)
M 37
80.4%
W 6
 
13.0%
N 1
 
2.2%
O 1
 
2.2%
P 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 3
75.0%
: 1
 
25.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 212
92.6%
Common 17
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 54
25.5%
a 45
21.2%
M 37
17.5%
o 16
 
7.5%
r 11
 
5.2%
e 8
 
3.8%
m 7
 
3.3%
W 6
 
2.8%
i 3
 
1.4%
y 3
 
1.4%
Other values (13) 22
10.4%
Common
ValueCountFrequency (%)
11
64.7%
, 3
 
17.6%
- 2
 
11.8%
: 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 54
23.6%
a 45
19.7%
M 37
16.2%
o 16
 
7.0%
r 11
 
4.8%
11
 
4.8%
e 8
 
3.5%
m 7
 
3.1%
W 6
 
2.6%
, 3
 
1.3%
Other values (17) 31
13.5%

Trans
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)6.5%
Missing4
Missing (%)8.0%
Memory size528.0 B
No
43 
Yes
 
2
Or, in your own words:
 
1

Length

Max length22
Median length2
Mean length2.4782609
Min length2

Characters and Unicode

Total characters114
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.2%

Sample

1st rowNo
2nd rowNo
3rd rowOr, in your own words:
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 43
86.0%
Yes 2
 
4.0%
Or, in your own words: 1
 
2.0%
(Missing) 4
 
8.0%

Length

2023-05-21T23:36:35.623678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:35.966713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 43
86.0%
yes 2
 
4.0%
or 1
 
2.0%
in 1
 
2.0%
your 1
 
2.0%
own 1
 
2.0%
words 1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
o 46
40.4%
N 43
37.7%
4
 
3.5%
s 3
 
2.6%
r 3
 
2.6%
Y 2
 
1.8%
e 2
 
1.8%
n 2
 
1.8%
w 2
 
1.8%
O 1
 
0.9%
Other values (6) 6
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 62
54.4%
Uppercase Letter 46
40.4%
Space Separator 4
 
3.5%
Other Punctuation 2
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 46
74.2%
s 3
 
4.8%
r 3
 
4.8%
e 2
 
3.2%
n 2
 
3.2%
w 2
 
3.2%
i 1
 
1.6%
y 1
 
1.6%
u 1
 
1.6%
d 1
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
N 43
93.5%
Y 2
 
4.3%
O 1
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
: 1
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 108
94.7%
Common 6
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 46
42.6%
N 43
39.8%
s 3
 
2.8%
r 3
 
2.8%
Y 2
 
1.9%
e 2
 
1.9%
n 2
 
1.9%
w 2
 
1.9%
O 1
 
0.9%
i 1
 
0.9%
Other values (3) 3
 
2.8%
Common
ValueCountFrequency (%)
4
66.7%
, 1
 
16.7%
: 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 46
40.4%
N 43
37.7%
4
 
3.5%
s 3
 
2.6%
r 3
 
2.6%
Y 2
 
1.8%
e 2
 
1.8%
n 2
 
1.8%
w 2
 
1.8%
O 1
 
0.9%
Other values (6) 6
 
5.3%

Sexuality
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)15.9%
Missing6
Missing (%)12.0%
Memory size528.0 B
Straight / Heterosexual
32 
Bisexual
Prefer to self-describe:
 
3
Bisexual;Straight / Heterosexual
 
2
Gay or Lesbian
 
1
Other values (2)
 
2

Length

Max length32
Median length23
Mean length21.931818
Min length8

Characters and Unicode

Total characters965
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.8%

Sample

1st rowBisexual
2nd rowStraight / Heterosexual
3rd rowPrefer to self-describe:
4th rowStraight / Heterosexual
5th rowPrefer to self-describe:

Common Values

ValueCountFrequency (%)
Straight / Heterosexual 32
64.0%
Bisexual 4
 
8.0%
Prefer to self-describe: 3
 
6.0%
Bisexual;Straight / Heterosexual 2
 
4.0%
Gay or Lesbian 1
 
2.0%
Prefer to self-describe:;Queer 1
 
2.0%
Prefer not to say 1
 
2.0%
(Missing) 6
 
12.0%

Length

2023-05-21T23:36:36.294986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:36.624627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
34
27.2%
heterosexual 34
27.2%
straight 32
25.6%
prefer 5
 
4.0%
to 5
 
4.0%
bisexual 4
 
3.2%
self-describe 3
 
2.4%
bisexual;straight 2
 
1.6%
gay 1
 
0.8%
or 1
 
0.8%
Other values (4) 4
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 133
13.8%
t 108
11.2%
r 84
 
8.7%
81
 
8.4%
a 77
 
8.0%
s 50
 
5.2%
i 45
 
4.7%
l 44
 
4.6%
o 41
 
4.2%
u 41
 
4.2%
Other values (20) 261
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 757
78.4%
Uppercase Letter 82
 
8.5%
Space Separator 81
 
8.4%
Other Punctuation 41
 
4.2%
Dash Punctuation 4
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 133
17.6%
t 108
14.3%
r 84
11.1%
a 77
10.2%
s 50
 
6.6%
i 45
 
5.9%
l 44
 
5.8%
o 41
 
5.4%
u 41
 
5.4%
x 40
 
5.3%
Other values (8) 94
12.4%
Uppercase Letter
ValueCountFrequency (%)
S 34
41.5%
H 34
41.5%
B 6
 
7.3%
P 5
 
6.1%
G 1
 
1.2%
L 1
 
1.2%
Q 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 34
82.9%
: 4
 
9.8%
; 3
 
7.3%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 839
86.9%
Common 126
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 133
15.9%
t 108
12.9%
r 84
10.0%
a 77
9.2%
s 50
 
6.0%
i 45
 
5.4%
l 44
 
5.2%
o 41
 
4.9%
u 41
 
4.9%
x 40
 
4.8%
Other values (15) 176
21.0%
Common
ValueCountFrequency (%)
81
64.3%
/ 34
27.0%
: 4
 
3.2%
- 4
 
3.2%
; 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 133
13.8%
t 108
11.2%
r 84
 
8.7%
81
 
8.4%
a 77
 
8.0%
s 50
 
5.2%
i 45
 
4.7%
l 44
 
4.6%
o 41
 
4.2%
u 41
 
4.2%
Other values (20) 261
27.0%

Ethnicity
Categorical

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)29.5%
Missing6
Missing (%)12.0%
Memory size528.0 B
European
11 
White
10 
White;European
Middle Eastern
White;North American
Other values (8)

Length

Max length50
Median length31
Mean length12.136364
Min length5

Characters and Unicode

Total characters534
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)15.9%

Sample

1st rowWhite
2nd rowWhite
3rd rowOr, in your own words:
4th rowIndian
5th rowIndian

Common Values

ValueCountFrequency (%)
European 11
22.0%
White 10
20.0%
White;European 7
14.0%
Middle Eastern 4
 
8.0%
White;North American 3
 
6.0%
Indian 2
 
4.0%
Or, in your own words: 1
 
2.0%
White;European;Middle Eastern;Ethnoreligious group 1
 
2.0%
Prefer not to say 1
 
2.0%
European;Ethnoreligious group 1
 
2.0%
Other values (3) 3
 
6.0%
(Missing) 6
12.0%

Length

2023-05-21T23:36:37.002997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
european 11
17.5%
white 10
15.9%
white;european 7
11.1%
middle 4
 
6.3%
eastern 4
 
6.3%
american 4
 
6.3%
white;north 3
 
4.8%
indian 2
 
3.2%
group 2
 
3.2%
not 1
 
1.6%
Other values (15) 15
23.8%

Most occurring characters

ValueCountFrequency (%)
e 61
 
11.4%
r 44
 
8.2%
n 42
 
7.9%
i 41
 
7.7%
a 37
 
6.9%
t 36
 
6.7%
o 36
 
6.7%
E 29
 
5.4%
h 28
 
5.2%
u 26
 
4.9%
Other values (21) 154
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 425
79.6%
Uppercase Letter 71
 
13.3%
Space Separator 19
 
3.6%
Other Punctuation 19
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 61
14.4%
r 44
10.4%
n 42
9.9%
i 41
9.6%
a 37
8.7%
t 36
8.5%
o 36
8.5%
h 28
6.6%
u 26
6.1%
p 23
 
5.4%
Other values (9) 51
12.0%
Uppercase Letter
ValueCountFrequency (%)
E 29
40.8%
W 22
31.0%
A 7
 
9.9%
M 5
 
7.0%
N 4
 
5.6%
I 2
 
2.8%
O 1
 
1.4%
P 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
; 17
89.5%
, 1
 
5.3%
: 1
 
5.3%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 496
92.9%
Common 38
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 61
12.3%
r 44
 
8.9%
n 42
 
8.5%
i 41
 
8.3%
a 37
 
7.5%
t 36
 
7.3%
o 36
 
7.3%
E 29
 
5.8%
h 28
 
5.6%
u 26
 
5.2%
Other values (17) 116
23.4%
Common
ValueCountFrequency (%)
19
50.0%
; 17
44.7%
, 1
 
2.6%
: 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 61
 
11.4%
r 44
 
8.2%
n 42
 
7.9%
i 41
 
7.7%
a 37
 
6.9%
t 36
 
6.7%
o 36
 
6.7%
E 29
 
5.4%
h 28
 
5.2%
u 26
 
4.9%
Other values (21) 154
28.8%

Accessibility
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)7.0%
Missing7
Missing (%)14.0%
Memory size528.0 B
None of the above
40 
Or, in your own words:
 
2
I am deaf / hard of hearing
 
1

Length

Max length27
Median length17
Mean length17.465116
Min length17

Characters and Unicode

Total characters751
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st rowNone of the above
2nd rowNone of the above
3rd rowOr, in your own words:
4th rowNone of the above
5th rowNone of the above

Common Values

ValueCountFrequency (%)
None of the above 40
80.0%
Or, in your own words: 2
 
4.0%
I am deaf / hard of hearing 1
 
2.0%
(Missing) 7
 
14.0%

Length

2023-05-21T23:36:37.215311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:37.464747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
of 41
23.2%
none 40
22.6%
the 40
22.6%
above 40
22.6%
or 2
 
1.1%
in 2
 
1.1%
your 2
 
1.1%
own 2
 
1.1%
words 2
 
1.1%
i 1
 
0.6%
Other values (5) 5
 
2.8%

Most occurring characters

ValueCountFrequency (%)
134
17.8%
o 127
16.9%
e 122
16.2%
n 45
 
6.0%
a 44
 
5.9%
f 42
 
5.6%
h 42
 
5.6%
N 40
 
5.3%
t 40
 
5.3%
b 40
 
5.3%
Other values (15) 75
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 569
75.8%
Space Separator 134
 
17.8%
Uppercase Letter 43
 
5.7%
Other Punctuation 5
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 127
22.3%
e 122
21.4%
n 45
 
7.9%
a 44
 
7.7%
f 42
 
7.4%
h 42
 
7.4%
t 40
 
7.0%
b 40
 
7.0%
v 40
 
7.0%
r 8
 
1.4%
Other values (8) 19
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
N 40
93.0%
O 2
 
4.7%
I 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 2
40.0%
: 2
40.0%
/ 1
20.0%
Space Separator
ValueCountFrequency (%)
134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 612
81.5%
Common 139
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 127
20.8%
e 122
19.9%
n 45
 
7.4%
a 44
 
7.2%
f 42
 
6.9%
h 42
 
6.9%
N 40
 
6.5%
t 40
 
6.5%
b 40
 
6.5%
v 40
 
6.5%
Other values (11) 30
 
4.9%
Common
ValueCountFrequency (%)
134
96.4%
, 2
 
1.4%
: 2
 
1.4%
/ 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
134
17.8%
o 127
16.9%
e 122
16.2%
n 45
 
6.0%
a 44
 
5.9%
f 42
 
5.6%
h 42
 
5.6%
N 40
 
5.3%
t 40
 
5.3%
b 40
 
5.3%
Other values (15) 75
10.0%

MentalHealth
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)18.6%
Missing7
Missing (%)14.0%
Memory size528.0 B
None of the above
28 
I have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.)
I have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.);I have an anxiety disorder
Or, in your own words:
 
2
I have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.);I have an anxiety disorder;I have a concentration and/or memory disorder (e.g., ADHD, etc.)
 
2
Other values (3)

Length

Max length170
Median length17
Mean length40.255814
Min length17

Characters and Unicode

Total characters1731
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.7%

Sample

1st rowI have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.);I have an anxiety disorder
2nd rowNone of the above
3rd rowOr, in your own words:
4th rowNone of the above
5th rowNone of the above

Common Values

ValueCountFrequency (%)
None of the above 28
56.0%
I have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.) 4
 
8.0%
I have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.);I have an anxiety disorder 3
 
6.0%
Or, in your own words: 2
 
4.0%
I have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.);I have an anxiety disorder;I have a concentration and/or memory disorder (e.g., ADHD, etc.) 2
 
4.0%
I have an anxiety disorder 2
 
4.0%
I have a concentration and/or memory disorder (e.g., ADHD, etc.);I have learning differences (e.g., Dyslexic, Dyslexia, etc.) 1
 
2.0%
I have autism / an autism spectrum disorder (e.g. Asperger's, etc.) 1
 
2.0%
(Missing) 7
 
14.0%

Length

2023-05-21T23:36:37.745709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:38.092858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
none 28
 
9.2%
the 28
 
9.2%
above 28
 
9.2%
of 28
 
9.2%
disorder 27
 
8.8%
have 21
 
6.9%
e.g 14
 
4.6%
i 13
 
4.2%
a 12
 
3.9%
or 11
 
3.6%
Other values (25) 96
31.4%

Most occurring characters

ValueCountFrequency (%)
263
15.2%
e 211
12.2%
o 194
 
11.2%
r 105
 
6.1%
a 104
 
6.0%
d 82
 
4.7%
n 80
 
4.6%
i 74
 
4.3%
t 67
 
3.9%
s 57
 
3.3%
Other values (27) 494
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1278
73.8%
Space Separator 263
 
15.2%
Other Punctuation 96
 
5.5%
Uppercase Letter 66
 
3.8%
Open Punctuation 14
 
0.8%
Close Punctuation 14
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 211
16.5%
o 194
15.2%
r 105
8.2%
a 104
8.1%
d 82
 
6.4%
n 80
 
6.3%
i 74
 
5.8%
t 67
 
5.2%
s 57
 
4.5%
h 49
 
3.8%
Other values (12) 255
20.0%
Other Punctuation
ValueCountFrequency (%)
. 42
43.8%
, 39
40.6%
; 8
 
8.3%
/ 4
 
4.2%
: 2
 
2.1%
' 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
N 28
42.4%
I 21
31.8%
D 8
 
12.1%
A 4
 
6.1%
H 3
 
4.5%
O 2
 
3.0%
Space Separator
ValueCountFrequency (%)
263
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1344
77.6%
Common 387
 
22.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 211
15.7%
o 194
14.4%
r 105
 
7.8%
a 104
 
7.7%
d 82
 
6.1%
n 80
 
6.0%
i 74
 
5.5%
t 67
 
5.0%
s 57
 
4.2%
h 49
 
3.6%
Other values (18) 321
23.9%
Common
ValueCountFrequency (%)
263
68.0%
. 42
 
10.9%
, 39
 
10.1%
( 14
 
3.6%
) 14
 
3.6%
; 8
 
2.1%
/ 4
 
1.0%
: 2
 
0.5%
' 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
263
15.2%
e 211
12.2%
o 194
 
11.2%
r 105
 
6.1%
a 104
 
6.0%
d 82
 
4.7%
n 80
 
4.6%
i 74
 
4.3%
t 67
 
3.9%
s 57
 
3.3%
Other values (27) 494
28.5%

TBranch
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)6.5%
Missing19
Missing (%)38.0%
Memory size228.0 B
True
18 
False
13 
(Missing)
19 
ValueCountFrequency (%)
True 18
36.0%
False 13
26.0%
(Missing) 19
38.0%
2023-05-21T23:36:38.521886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

ICorPM
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)11.8%
Missing33
Missing (%)66.0%
Memory size528.0 B
Independent contributor
16 
People manager
 
1

Length

Max length23
Median length23
Mean length22.470588
Min length14

Characters and Unicode

Total characters382
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.9%

Sample

1st rowIndependent contributor
2nd rowPeople manager
3rd rowIndependent contributor
4th rowIndependent contributor
5th rowIndependent contributor

Common Values

ValueCountFrequency (%)
Independent contributor 16
32.0%
People manager 1
 
2.0%
(Missing) 33
66.0%

Length

2023-05-21T23:36:38.768133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:39.128928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
independent 16
47.1%
contributor 16
47.1%
people 1
 
2.9%
manager 1
 
2.9%

Most occurring characters

ValueCountFrequency (%)
n 65
17.0%
e 51
13.4%
t 48
12.6%
r 33
8.6%
o 33
8.6%
d 32
8.4%
p 17
 
4.5%
17
 
4.5%
b 16
 
4.2%
u 16
 
4.2%
Other values (8) 54
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 348
91.1%
Space Separator 17
 
4.5%
Uppercase Letter 17
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 65
18.7%
e 51
14.7%
t 48
13.8%
r 33
9.5%
o 33
9.5%
d 32
9.2%
p 17
 
4.9%
b 16
 
4.6%
u 16
 
4.6%
i 16
 
4.6%
Other values (5) 21
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
I 16
94.1%
P 1
 
5.9%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 365
95.5%
Common 17
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 65
17.8%
e 51
14.0%
t 48
13.2%
r 33
9.0%
o 33
9.0%
d 32
8.8%
p 17
 
4.7%
b 16
 
4.4%
u 16
 
4.4%
I 16
 
4.4%
Other values (7) 38
10.4%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 65
17.0%
e 51
13.4%
t 48
12.6%
r 33
8.6%
o 33
8.6%
d 32
8.4%
p 17
 
4.5%
17
 
4.5%
b 16
 
4.2%
u 16
 
4.2%
Other values (8) 54
14.1%

WorkExp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)66.7%
Missing32
Missing (%)64.0%
Infinite0
Infinite (%)0.0%
Mean10.833333
Minimum3
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-05-21T23:36:39.336992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median6.5
Q314.75
95-th percentile23.75
Maximum28
Range25
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation7.964997
Coefficient of variation (CV)0.73523049
Kurtosis-0.46446422
Mean10.833333
Median Absolute Deviation (MAD)3
Skewness0.92673388
Sum195
Variance63.441176
MonotonicityNot monotonic
2023-05-21T23:36:39.526691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 4
 
8.0%
6 2
 
4.0%
14 2
 
4.0%
3 2
 
4.0%
15 1
 
2.0%
4 1
 
2.0%
23 1
 
2.0%
9 1
 
2.0%
22 1
 
2.0%
21 1
 
2.0%
Other values (2) 2
 
4.0%
(Missing) 32
64.0%
ValueCountFrequency (%)
3 2
4.0%
4 1
 
2.0%
5 4
8.0%
6 2
4.0%
7 1
 
2.0%
9 1
 
2.0%
14 2
4.0%
15 1
 
2.0%
21 1
 
2.0%
22 1
 
2.0%
ValueCountFrequency (%)
28 1
 
2.0%
23 1
 
2.0%
22 1
 
2.0%
21 1
 
2.0%
15 1
 
2.0%
14 2
4.0%
9 1
 
2.0%
7 1
 
2.0%
6 2
4.0%
5 4
8.0%

Knowledge_1
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)16.7%
Missing32
Missing (%)64.0%
Memory size528.0 B
Agree
10 
Strongly agree
Neither agree nor disagree

Length

Max length26
Median length5
Mean length11
Min length5

Characters and Unicode

Total characters198
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowStrongly agree
3rd rowNeither agree nor disagree
4th rowAgree
5th rowAgree

Common Values

ValueCountFrequency (%)
Agree 10
 
20.0%
Strongly agree 5
 
10.0%
Neither agree nor disagree 3
 
6.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:39.729430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:40.032726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
agree 18
56.2%
strongly 5
 
15.6%
neither 3
 
9.4%
nor 3
 
9.4%
disagree 3
 
9.4%

Most occurring characters

ValueCountFrequency (%)
e 48
24.2%
r 32
16.2%
g 26
13.1%
14
 
7.1%
a 11
 
5.6%
A 10
 
5.1%
t 8
 
4.0%
o 8
 
4.0%
n 8
 
4.0%
i 6
 
3.0%
Other values (7) 27
13.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 166
83.8%
Uppercase Letter 18
 
9.1%
Space Separator 14
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48
28.9%
r 32
19.3%
g 26
15.7%
a 11
 
6.6%
t 8
 
4.8%
o 8
 
4.8%
n 8
 
4.8%
i 6
 
3.6%
l 5
 
3.0%
y 5
 
3.0%
Other values (3) 9
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 10
55.6%
S 5
27.8%
N 3
 
16.7%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 184
92.9%
Common 14
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48
26.1%
r 32
17.4%
g 26
14.1%
a 11
 
6.0%
A 10
 
5.4%
t 8
 
4.3%
o 8
 
4.3%
n 8
 
4.3%
i 6
 
3.3%
l 5
 
2.7%
Other values (6) 22
12.0%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 48
24.2%
r 32
16.2%
g 26
13.1%
14
 
7.1%
a 11
 
5.6%
A 10
 
5.1%
t 8
 
4.0%
o 8
 
4.0%
n 8
 
4.0%
i 6
 
3.0%
Other values (7) 27
13.6%

Knowledge_2
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing32
Missing (%)64.0%
Memory size528.0 B
Agree
Neither agree nor disagree
Disagree
Strongly agree

Length

Max length26
Median length14
Mean length12.5
Min length5

Characters and Unicode

Total characters225
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDisagree
2nd rowAgree
3rd rowDisagree
4th rowAgree
5th rowNeither agree nor disagree

Common Values

ValueCountFrequency (%)
Agree 7
 
14.0%
Neither agree nor disagree 5
 
10.0%
Disagree 4
 
8.0%
Strongly agree 2
 
4.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:40.332828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:40.726436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
agree 14
40.0%
disagree 9
25.7%
neither 5
 
14.3%
nor 5
 
14.3%
strongly 2
 
5.7%

Most occurring characters

ValueCountFrequency (%)
e 56
24.9%
r 35
15.6%
g 25
11.1%
17
 
7.6%
a 16
 
7.1%
i 14
 
6.2%
s 9
 
4.0%
o 7
 
3.1%
n 7
 
3.1%
A 7
 
3.1%
Other values (8) 32
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 190
84.4%
Uppercase Letter 18
 
8.0%
Space Separator 17
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 56
29.5%
r 35
18.4%
g 25
13.2%
a 16
 
8.4%
i 14
 
7.4%
s 9
 
4.7%
o 7
 
3.7%
n 7
 
3.7%
t 7
 
3.7%
h 5
 
2.6%
Other values (3) 9
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
A 7
38.9%
N 5
27.8%
D 4
22.2%
S 2
 
11.1%
Space Separator
ValueCountFrequency (%)
17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 208
92.4%
Common 17
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 56
26.9%
r 35
16.8%
g 25
12.0%
a 16
 
7.7%
i 14
 
6.7%
s 9
 
4.3%
o 7
 
3.4%
n 7
 
3.4%
A 7
 
3.4%
t 7
 
3.4%
Other values (7) 25
12.0%
Common
ValueCountFrequency (%)
17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 56
24.9%
r 35
15.6%
g 25
11.1%
17
 
7.6%
a 16
 
7.1%
i 14
 
6.2%
s 9
 
4.0%
o 7
 
3.1%
n 7
 
3.1%
A 7
 
3.1%
Other values (8) 32
14.2%

Knowledge_3
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)27.8%
Missing32
Missing (%)64.0%
Memory size528.0 B
Neither agree nor disagree
Agree
Disagree
Strongly agree
Strongly disagree

Length

Max length26
Median length17
Mean length14
Min length5

Characters and Unicode

Total characters252
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)11.1%

Sample

1st rowAgree
2nd rowDisagree
3rd rowStrongly agree
4th rowNeither agree nor disagree
5th rowNeither agree nor disagree

Common Values

ValueCountFrequency (%)
Neither agree nor disagree 6
 
12.0%
Agree 5
 
10.0%
Disagree 5
 
10.0%
Strongly agree 1
 
2.0%
Strongly disagree 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:41.059078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:41.423038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
agree 12
31.6%
disagree 12
31.6%
neither 6
15.8%
nor 6
15.8%
strongly 2
 
5.3%

Most occurring characters

ValueCountFrequency (%)
e 60
23.8%
r 38
15.1%
g 26
10.3%
20
 
7.9%
a 19
 
7.5%
i 18
 
7.1%
s 12
 
4.8%
n 8
 
3.2%
t 8
 
3.2%
o 8
 
3.2%
Other values (8) 35
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214
84.9%
Space Separator 20
 
7.9%
Uppercase Letter 18
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 60
28.0%
r 38
17.8%
g 26
12.1%
a 19
 
8.9%
i 18
 
8.4%
s 12
 
5.6%
n 8
 
3.7%
t 8
 
3.7%
o 8
 
3.7%
d 7
 
3.3%
Other values (3) 10
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
N 6
33.3%
A 5
27.8%
D 5
27.8%
S 2
 
11.1%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 232
92.1%
Common 20
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 60
25.9%
r 38
16.4%
g 26
11.2%
a 19
 
8.2%
i 18
 
7.8%
s 12
 
5.2%
n 8
 
3.4%
t 8
 
3.4%
o 8
 
3.4%
d 7
 
3.0%
Other values (7) 28
12.1%
Common
ValueCountFrequency (%)
20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 60
23.8%
r 38
15.1%
g 26
10.3%
20
 
7.9%
a 19
 
7.5%
i 18
 
7.1%
s 12
 
4.8%
n 8
 
3.2%
t 8
 
3.2%
o 8
 
3.2%
Other values (8) 35
13.9%

Knowledge_4
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing32
Missing (%)64.0%
Memory size528.0 B
Agree
Disagree
Neither agree nor disagree
Strongly agree

Length

Max length26
Median length14
Mean length12
Min length5

Characters and Unicode

Total characters216
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowAgree
3rd rowStrongly agree
4th rowNeither agree nor disagree
5th rowNeither agree nor disagree

Common Values

ValueCountFrequency (%)
Agree 6
 
12.0%
Disagree 5
 
10.0%
Neither agree nor disagree 4
 
8.0%
Strongly agree 3
 
6.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:41.774399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:42.111266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
agree 13
39.4%
disagree 9
27.3%
neither 4
 
12.1%
nor 4
 
12.1%
strongly 3
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e 52
24.1%
r 33
15.3%
g 25
11.6%
a 16
 
7.4%
15
 
6.9%
i 13
 
6.0%
s 9
 
4.2%
t 7
 
3.2%
o 7
 
3.2%
n 7
 
3.2%
Other values (8) 32
14.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 183
84.7%
Uppercase Letter 18
 
8.3%
Space Separator 15
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 52
28.4%
r 33
18.0%
g 25
13.7%
a 16
 
8.7%
i 13
 
7.1%
s 9
 
4.9%
t 7
 
3.8%
o 7
 
3.8%
n 7
 
3.8%
h 4
 
2.2%
Other values (3) 10
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
A 6
33.3%
D 5
27.8%
N 4
22.2%
S 3
16.7%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 201
93.1%
Common 15
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 52
25.9%
r 33
16.4%
g 25
12.4%
a 16
 
8.0%
i 13
 
6.5%
s 9
 
4.5%
t 7
 
3.5%
o 7
 
3.5%
n 7
 
3.5%
A 6
 
3.0%
Other values (7) 26
12.9%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 52
24.1%
r 33
15.3%
g 25
11.6%
a 16
 
7.4%
15
 
6.9%
i 13
 
6.0%
s 9
 
4.2%
t 7
 
3.2%
o 7
 
3.2%
n 7
 
3.2%
Other values (8) 32
14.8%

Knowledge_5
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing32
Missing (%)64.0%
Memory size528.0 B
Agree
Strongly agree
Neither agree nor disagree
Disagree

Length

Max length26
Median length14
Mean length12.333333
Min length5

Characters and Unicode

Total characters222
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowAgree
3rd rowStrongly agree
4th rowNeither agree nor disagree
5th rowNeither agree nor disagree

Common Values

ValueCountFrequency (%)
Agree 6
 
12.0%
Strongly agree 4
 
8.0%
Neither agree nor disagree 4
 
8.0%
Disagree 4
 
8.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:42.409506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:42.732748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
agree 14
41.2%
disagree 8
23.5%
strongly 4
 
11.8%
neither 4
 
11.8%
nor 4
 
11.8%

Most occurring characters

ValueCountFrequency (%)
e 52
23.4%
r 34
15.3%
g 26
11.7%
16
 
7.2%
a 16
 
7.2%
i 12
 
5.4%
s 8
 
3.6%
t 8
 
3.6%
o 8
 
3.6%
n 8
 
3.6%
Other values (8) 34
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 188
84.7%
Uppercase Letter 18
 
8.1%
Space Separator 16
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 52
27.7%
r 34
18.1%
g 26
13.8%
a 16
 
8.5%
i 12
 
6.4%
s 8
 
4.3%
t 8
 
4.3%
o 8
 
4.3%
n 8
 
4.3%
d 4
 
2.1%
Other values (3) 12
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
A 6
33.3%
N 4
22.2%
S 4
22.2%
D 4
22.2%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206
92.8%
Common 16
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 52
25.2%
r 34
16.5%
g 26
12.6%
a 16
 
7.8%
i 12
 
5.8%
s 8
 
3.9%
t 8
 
3.9%
o 8
 
3.9%
n 8
 
3.9%
A 6
 
2.9%
Other values (7) 28
13.6%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 52
23.4%
r 34
15.3%
g 26
11.7%
16
 
7.2%
a 16
 
7.2%
i 12
 
5.4%
s 8
 
3.6%
t 8
 
3.6%
o 8
 
3.6%
n 8
 
3.6%
Other values (8) 34
15.3%

Knowledge_6
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing32
Missing (%)64.0%
Memory size528.0 B
Agree
Strongly agree
Disagree
Neither agree nor disagree

Length

Max length26
Median length20
Mean length9.8333333
Min length5

Characters and Unicode

Total characters177
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAgree
2nd rowAgree
3rd rowAgree
4th rowAgree
5th rowNeither agree nor disagree

Common Values

ValueCountFrequency (%)
Agree 9
 
18.0%
Strongly agree 4
 
8.0%
Disagree 3
 
6.0%
Neither agree nor disagree 2
 
4.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:43.039686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:43.353866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
agree 15
53.6%
disagree 5
 
17.9%
strongly 4
 
14.3%
neither 2
 
7.1%
nor 2
 
7.1%

Most occurring characters

ValueCountFrequency (%)
e 44
24.9%
r 28
15.8%
g 24
13.6%
a 11
 
6.2%
10
 
5.6%
A 9
 
5.1%
i 7
 
4.0%
t 6
 
3.4%
o 6
 
3.4%
n 6
 
3.4%
Other values (8) 26
14.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149
84.2%
Uppercase Letter 18
 
10.2%
Space Separator 10
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 44
29.5%
r 28
18.8%
g 24
16.1%
a 11
 
7.4%
i 7
 
4.7%
t 6
 
4.0%
o 6
 
4.0%
n 6
 
4.0%
s 5
 
3.4%
y 4
 
2.7%
Other values (3) 8
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 9
50.0%
S 4
22.2%
D 3
 
16.7%
N 2
 
11.1%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 167
94.4%
Common 10
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 44
26.3%
r 28
16.8%
g 24
14.4%
a 11
 
6.6%
A 9
 
5.4%
i 7
 
4.2%
t 6
 
3.6%
o 6
 
3.6%
n 6
 
3.6%
s 5
 
3.0%
Other values (7) 21
12.6%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 44
24.9%
r 28
15.8%
g 24
13.6%
a 11
 
6.2%
10
 
5.6%
A 9
 
5.1%
i 7
 
4.0%
t 6
 
3.4%
o 6
 
3.4%
n 6
 
3.4%
Other values (8) 26
14.7%

Knowledge_7
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing32
Missing (%)64.0%
Memory size528.0 B
Agree
Disagree
Neither agree nor disagree
Strongly agree

Length

Max length26
Median length14
Mean length10
Min length5

Characters and Unicode

Total characters180
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.6%

Sample

1st rowDisagree
2nd rowAgree
3rd rowDisagree
4th rowAgree
5th rowNeither agree nor disagree

Common Values

ValueCountFrequency (%)
Agree 8
 
16.0%
Disagree 6
 
12.0%
Neither agree nor disagree 3
 
6.0%
Strongly agree 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:43.612704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:43.917838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
agree 12
42.9%
disagree 9
32.1%
neither 3
 
10.7%
nor 3
 
10.7%
strongly 1
 
3.6%

Most occurring characters

ValueCountFrequency (%)
e 48
26.7%
r 28
15.6%
g 22
12.2%
a 13
 
7.2%
i 12
 
6.7%
10
 
5.6%
s 9
 
5.0%
A 8
 
4.4%
D 6
 
3.3%
o 4
 
2.2%
Other values (8) 20
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 152
84.4%
Uppercase Letter 18
 
10.0%
Space Separator 10
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48
31.6%
r 28
18.4%
g 22
14.5%
a 13
 
8.6%
i 12
 
7.9%
s 9
 
5.9%
o 4
 
2.6%
n 4
 
2.6%
t 4
 
2.6%
h 3
 
2.0%
Other values (3) 5
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
A 8
44.4%
D 6
33.3%
N 3
 
16.7%
S 1
 
5.6%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 170
94.4%
Common 10
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48
28.2%
r 28
16.5%
g 22
12.9%
a 13
 
7.6%
i 12
 
7.1%
s 9
 
5.3%
A 8
 
4.7%
D 6
 
3.5%
o 4
 
2.4%
n 4
 
2.4%
Other values (7) 16
 
9.4%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 48
26.7%
r 28
15.6%
g 22
12.2%
a 13
 
7.2%
i 12
 
6.7%
10
 
5.6%
s 9
 
5.0%
A 8
 
4.4%
D 6
 
3.3%
o 4
 
2.2%
Other values (8) 20
11.1%

Frequency_1
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing32
Missing (%)64.0%
Memory size528.0 B
1-2 times a week
13 
Never
3-5 times a week
 
1
10+ times a week
 
1

Length

Max length16
Median length16
Mean length14.166667
Min length5

Characters and Unicode

Total characters255
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)11.1%

Sample

1st row3-5 times a week
2nd row10+ times a week
3rd rowNever
4th row1-2 times a week
5th row1-2 times a week

Common Values

ValueCountFrequency (%)
1-2 times a week 13
26.0%
Never 3
 
6.0%
3-5 times a week 1
 
2.0%
10+ times a week 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:44.206009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:44.525280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
times 15
23.8%
a 15
23.8%
week 15
23.8%
1-2 13
20.6%
never 3
 
4.8%
3-5 1
 
1.6%
10 1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 51
20.0%
45
17.6%
a 15
 
5.9%
w 15
 
5.9%
k 15
 
5.9%
t 15
 
5.9%
i 15
 
5.9%
m 15
 
5.9%
s 15
 
5.9%
1 14
 
5.5%
Other values (9) 40
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162
63.5%
Space Separator 45
 
17.6%
Decimal Number 30
 
11.8%
Dash Punctuation 14
 
5.5%
Uppercase Letter 3
 
1.2%
Math Symbol 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 51
31.5%
a 15
 
9.3%
w 15
 
9.3%
k 15
 
9.3%
t 15
 
9.3%
i 15
 
9.3%
m 15
 
9.3%
s 15
 
9.3%
v 3
 
1.9%
r 3
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 14
46.7%
2 13
43.3%
3 1
 
3.3%
5 1
 
3.3%
0 1
 
3.3%
Space Separator
ValueCountFrequency (%)
45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 165
64.7%
Common 90
35.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 51
30.9%
a 15
 
9.1%
w 15
 
9.1%
k 15
 
9.1%
t 15
 
9.1%
i 15
 
9.1%
m 15
 
9.1%
s 15
 
9.1%
N 3
 
1.8%
v 3
 
1.8%
Common
ValueCountFrequency (%)
45
50.0%
1 14
 
15.6%
- 14
 
15.6%
2 13
 
14.4%
3 1
 
1.1%
5 1
 
1.1%
0 1
 
1.1%
+ 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 51
20.0%
45
17.6%
a 15
 
5.9%
w 15
 
5.9%
k 15
 
5.9%
t 15
 
5.9%
i 15
 
5.9%
m 15
 
5.9%
s 15
 
5.9%
1 14
 
5.5%
Other values (9) 40
15.7%

Frequency_2
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)27.8%
Missing32
Missing (%)64.0%
Memory size528.0 B
1-2 times a week
3-5 times a week
10+ times a week
6-10 times a week
Never

Length

Max length17
Median length16
Mean length15.5
Min length5

Characters and Unicode

Total characters279
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.6%

Sample

1st row3-5 times a week
2nd row10+ times a week
3rd rowNever
4th row10+ times a week
5th row3-5 times a week

Common Values

ValueCountFrequency (%)
1-2 times a week 9
 
18.0%
3-5 times a week 3
 
6.0%
10+ times a week 3
 
6.0%
6-10 times a week 2
 
4.0%
Never 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:44.791893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:45.137773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
times 17
24.6%
a 17
24.6%
week 17
24.6%
1-2 9
13.0%
3-5 3
 
4.3%
10 3
 
4.3%
6-10 2
 
2.9%
never 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 53
19.0%
51
18.3%
w 17
 
6.1%
k 17
 
6.1%
t 17
 
6.1%
i 17
 
6.1%
m 17
 
6.1%
s 17
 
6.1%
a 17
 
6.1%
- 14
 
5.0%
Other values (10) 42
15.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 174
62.4%
Space Separator 51
 
18.3%
Decimal Number 36
 
12.9%
Dash Punctuation 14
 
5.0%
Math Symbol 3
 
1.1%
Uppercase Letter 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 53
30.5%
w 17
 
9.8%
k 17
 
9.8%
t 17
 
9.8%
i 17
 
9.8%
m 17
 
9.8%
s 17
 
9.8%
a 17
 
9.8%
v 1
 
0.6%
r 1
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 14
38.9%
2 9
25.0%
0 5
 
13.9%
3 3
 
8.3%
5 3
 
8.3%
6 2
 
5.6%
Space Separator
ValueCountFrequency (%)
51
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 175
62.7%
Common 104
37.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 53
30.3%
w 17
 
9.7%
k 17
 
9.7%
t 17
 
9.7%
i 17
 
9.7%
m 17
 
9.7%
s 17
 
9.7%
a 17
 
9.7%
N 1
 
0.6%
v 1
 
0.6%
Common
ValueCountFrequency (%)
51
49.0%
- 14
 
13.5%
1 14
 
13.5%
2 9
 
8.7%
0 5
 
4.8%
3 3
 
2.9%
5 3
 
2.9%
+ 3
 
2.9%
6 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 53
19.0%
51
18.3%
w 17
 
6.1%
k 17
 
6.1%
t 17
 
6.1%
i 17
 
6.1%
m 17
 
6.1%
s 17
 
6.1%
a 17
 
6.1%
- 14
 
5.0%
Other values (10) 42
15.1%

Frequency_3
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)16.7%
Missing32
Missing (%)64.0%
Memory size528.0 B
1-2 times a week
11 
Never
3-5 times a week

Length

Max length16
Median length16
Mean length12.944444
Min length5

Characters and Unicode

Total characters233
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNever
2nd row3-5 times a week
3rd rowNever
4th row1-2 times a week
5th row1-2 times a week

Common Values

ValueCountFrequency (%)
1-2 times a week 11
 
22.0%
Never 5
 
10.0%
3-5 times a week 2
 
4.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:45.357996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:45.672714image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
times 13
22.8%
a 13
22.8%
week 13
22.8%
1-2 11
19.3%
never 5
 
8.8%
3-5 2
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e 49
21.0%
39
16.7%
s 13
 
5.6%
w 13
 
5.6%
k 13
 
5.6%
t 13
 
5.6%
i 13
 
5.6%
m 13
 
5.6%
- 13
 
5.6%
a 13
 
5.6%
Other values (7) 41
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 150
64.4%
Space Separator 39
 
16.7%
Decimal Number 26
 
11.2%
Dash Punctuation 13
 
5.6%
Uppercase Letter 5
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 49
32.7%
s 13
 
8.7%
w 13
 
8.7%
k 13
 
8.7%
t 13
 
8.7%
i 13
 
8.7%
m 13
 
8.7%
a 13
 
8.7%
v 5
 
3.3%
r 5
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 11
42.3%
2 11
42.3%
3 2
 
7.7%
5 2
 
7.7%
Space Separator
ValueCountFrequency (%)
39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 155
66.5%
Common 78
33.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 49
31.6%
s 13
 
8.4%
w 13
 
8.4%
k 13
 
8.4%
t 13
 
8.4%
i 13
 
8.4%
m 13
 
8.4%
a 13
 
8.4%
N 5
 
3.2%
v 5
 
3.2%
Common
ValueCountFrequency (%)
39
50.0%
- 13
 
16.7%
1 11
 
14.1%
2 11
 
14.1%
3 2
 
2.6%
5 2
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 49
21.0%
39
16.7%
s 13
 
5.6%
w 13
 
5.6%
k 13
 
5.6%
t 13
 
5.6%
i 13
 
5.6%
m 13
 
5.6%
- 13
 
5.6%
a 13
 
5.6%
Other values (7) 41
17.6%

TimeSearching
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing32
Missing (%)64.0%
Memory size528.0 B
30-60 minutes a day
13 
15-30 minutes a day
60-120 minutes a day
 
1
Less than 15 minutes a day
 
1

Length

Max length26
Median length19
Mean length19.444444
Min length19

Characters and Unicode

Total characters350
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)11.1%

Sample

1st row15-30 minutes a day
2nd row30-60 minutes a day
3rd row30-60 minutes a day
4th row60-120 minutes a day
5th row30-60 minutes a day

Common Values

ValueCountFrequency (%)
30-60 minutes a day 13
26.0%
15-30 minutes a day 3
 
6.0%
60-120 minutes a day 1
 
2.0%
Less than 15 minutes a day 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:45.977619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:46.295257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
minutes 18
24.3%
a 18
24.3%
day 18
24.3%
30-60 13
17.6%
15-30 3
 
4.1%
60-120 1
 
1.4%
less 1
 
1.4%
than 1
 
1.4%
15 1
 
1.4%

Most occurring characters

ValueCountFrequency (%)
56
16.0%
a 37
 
10.6%
0 31
 
8.9%
s 20
 
5.7%
e 19
 
5.4%
n 19
 
5.4%
t 19
 
5.4%
d 18
 
5.1%
y 18
 
5.1%
m 18
 
5.1%
Other values (10) 95
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 205
58.6%
Decimal Number 71
 
20.3%
Space Separator 56
 
16.0%
Dash Punctuation 17
 
4.9%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 37
18.0%
s 20
9.8%
e 19
9.3%
n 19
9.3%
t 19
9.3%
d 18
8.8%
y 18
8.8%
m 18
8.8%
i 18
8.8%
u 18
8.8%
Decimal Number
ValueCountFrequency (%)
0 31
43.7%
3 16
22.5%
6 14
19.7%
1 5
 
7.0%
5 4
 
5.6%
2 1
 
1.4%
Space Separator
ValueCountFrequency (%)
56
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Uppercase Letter
ValueCountFrequency (%)
L 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206
58.9%
Common 144
41.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 37
18.0%
s 20
9.7%
e 19
9.2%
n 19
9.2%
t 19
9.2%
d 18
8.7%
y 18
8.7%
m 18
8.7%
i 18
8.7%
u 18
8.7%
Other values (2) 2
 
1.0%
Common
ValueCountFrequency (%)
56
38.9%
0 31
21.5%
- 17
 
11.8%
3 16
 
11.1%
6 14
 
9.7%
1 5
 
3.5%
5 4
 
2.8%
2 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56
16.0%
a 37
 
10.6%
0 31
 
8.9%
s 20
 
5.7%
e 19
 
5.4%
n 19
 
5.4%
t 19
 
5.4%
d 18
 
5.1%
y 18
 
5.1%
m 18
 
5.1%
Other values (10) 95
27.1%

TimeAnswering
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)27.8%
Missing32
Missing (%)64.0%
Memory size528.0 B
15-30 minutes a day
30-60 minutes a day
60-120 minutes a day
Less than 15 minutes a day
Over 120 minutes a day

Length

Max length26
Median length19
Mean length20.111111
Min length19

Characters and Unicode

Total characters362
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.6%

Sample

1st rowOver 120 minutes a day
2nd row60-120 minutes a day
3rd rowLess than 15 minutes a day
4th row30-60 minutes a day
5th row30-60 minutes a day

Common Values

ValueCountFrequency (%)
15-30 minutes a day 8
 
16.0%
30-60 minutes a day 4
 
8.0%
60-120 minutes a day 3
 
6.0%
Less than 15 minutes a day 2
 
4.0%
Over 120 minutes a day 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:46.555007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:46.904133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
minutes 18
23.4%
a 18
23.4%
day 18
23.4%
15-30 8
10.4%
30-60 4
 
5.2%
60-120 3
 
3.9%
less 2
 
2.6%
than 2
 
2.6%
15 2
 
2.6%
over 1
 
1.3%

Most occurring characters

ValueCountFrequency (%)
59
16.3%
a 38
 
10.5%
0 23
 
6.4%
s 22
 
6.1%
e 21
 
5.8%
n 20
 
5.5%
t 20
 
5.5%
y 18
 
5.0%
m 18
 
5.0%
i 18
 
5.0%
Other values (13) 105
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 215
59.4%
Decimal Number 70
 
19.3%
Space Separator 59
 
16.3%
Dash Punctuation 15
 
4.1%
Uppercase Letter 3
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 38
17.7%
s 22
10.2%
e 21
9.8%
n 20
9.3%
t 20
9.3%
y 18
8.4%
m 18
8.4%
i 18
8.4%
u 18
8.4%
d 18
8.4%
Other values (3) 4
 
1.9%
Decimal Number
ValueCountFrequency (%)
0 23
32.9%
1 14
20.0%
3 12
17.1%
5 10
14.3%
6 7
 
10.0%
2 4
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
L 2
66.7%
O 1
33.3%
Space Separator
ValueCountFrequency (%)
59
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 218
60.2%
Common 144
39.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 38
17.4%
s 22
10.1%
e 21
9.6%
n 20
9.2%
t 20
9.2%
y 18
8.3%
m 18
8.3%
i 18
8.3%
u 18
8.3%
d 18
8.3%
Other values (5) 7
 
3.2%
Common
ValueCountFrequency (%)
59
41.0%
0 23
 
16.0%
- 15
 
10.4%
1 14
 
9.7%
3 12
 
8.3%
5 10
 
6.9%
6 7
 
4.9%
2 4
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59
16.3%
a 38
 
10.5%
0 23
 
6.4%
s 22
 
6.1%
e 21
 
5.8%
n 20
 
5.5%
t 20
 
5.5%
y 18
 
5.0%
m 18
 
5.0%
i 18
 
5.0%
Other values (13) 105
29.0%

Onboarding
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)27.8%
Missing32
Missing (%)64.0%
Memory size528.0 B
Somewhat long
Somewhat short
Just right
Very long
Very short

Length

Max length14
Median length13.5
Mean length12.277778
Min length9

Characters and Unicode

Total characters221
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.6%

Sample

1st rowSomewhat long
2nd rowJust right
3rd rowSomewhat short
4th rowVery short
5th rowSomewhat long

Common Values

ValueCountFrequency (%)
Somewhat long 9
 
18.0%
Somewhat short 4
 
8.0%
Just right 2
 
4.0%
Very long 2
 
4.0%
Very short 1
 
2.0%
(Missing) 32
64.0%

Length

2023-05-21T23:36:47.236548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:47.543732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
somewhat 13
36.1%
long 11
30.6%
short 5
 
13.9%
very 3
 
8.3%
just 2
 
5.6%
right 2
 
5.6%

Most occurring characters

ValueCountFrequency (%)
o 29
13.1%
t 22
10.0%
h 20
 
9.0%
18
 
8.1%
e 16
 
7.2%
S 13
 
5.9%
g 13
 
5.9%
m 13
 
5.9%
w 13
 
5.9%
a 13
 
5.9%
Other values (9) 51
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 185
83.7%
Space Separator 18
 
8.1%
Uppercase Letter 18
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 29
15.7%
t 22
11.9%
h 20
10.8%
e 16
8.6%
g 13
7.0%
m 13
7.0%
w 13
7.0%
a 13
7.0%
l 11
 
5.9%
n 11
 
5.9%
Other values (5) 24
13.0%
Uppercase Letter
ValueCountFrequency (%)
S 13
72.2%
V 3
 
16.7%
J 2
 
11.1%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 203
91.9%
Common 18
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 29
14.3%
t 22
10.8%
h 20
9.9%
e 16
 
7.9%
S 13
 
6.4%
g 13
 
6.4%
m 13
 
6.4%
w 13
 
6.4%
a 13
 
6.4%
l 11
 
5.4%
Other values (8) 40
19.7%
Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 29
13.1%
t 22
10.0%
h 20
 
9.0%
18
 
8.1%
e 16
 
7.2%
S 13
 
5.9%
g 13
 
5.9%
m 13
 
5.9%
w 13
 
5.9%
a 13
 
5.9%
Other values (9) 51
23.1%

ProfessionalTech
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)82.4%
Missing33
Missing (%)66.0%
Memory size528.0 B
Continuous integration (CI) and (more often) continuous delivery;Automated testing
DevOps function;Microservices;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools
Innersource initiative;DevOps function;Microservices;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools
Innersource initiative;DevOps function;Microservices;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools
DevOps function;Microservices
Other values (9)

Length

Max length219
Median length132
Mean length101.82353
Min length13

Characters and Unicode

Total characters1731
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)70.6%

Sample

1st rowInnersource initiative;DevOps function;Microservices;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools
2nd rowInnersource initiative;DevOps function;Microservices;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools
3rd rowDevOps function;Microservices
4th rowContinuous integration (CI) and (more often) continuous delivery;Automated testing
5th rowDevOps function;Continuous integration (CI) and (more often) continuous delivery;Automated testing

Common Values

ValueCountFrequency (%)
Continuous integration (CI) and (more often) continuous delivery;Automated testing 3
 
6.0%
DevOps function;Microservices;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools 2
 
4.0%
Innersource initiative;DevOps function;Microservices;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools 1
 
2.0%
Innersource initiative;DevOps function;Microservices;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools 1
 
2.0%
DevOps function;Microservices 1
 
2.0%
DevOps function;Continuous integration (CI) and (more often) continuous delivery;Automated testing 1
 
2.0%
None of these 1
 
2.0%
Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery;Automated testing 1
 
2.0%
DevOps function;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability tools 1
 
2.0%
DevOps function 1
 
2.0%
Other values (4) 4
 
8.0%
(Missing) 33
66.0%

Length

2023-05-21T23:36:47.837997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
continuous 16
 
9.5%
integration 12
 
7.1%
ci 12
 
7.1%
and 12
 
7.1%
more 12
 
7.1%
often 12
 
7.1%
delivery;automated 12
 
7.1%
devops 8
 
4.8%
testing;observability 8
 
4.8%
tools 8
 
4.8%
Other values (23) 56
33.3%

Most occurring characters

ValueCountFrequency (%)
t 163
 
9.4%
o 162
 
9.4%
e 159
 
9.2%
151
 
8.7%
n 144
 
8.3%
i 129
 
7.5%
s 96
 
5.5%
r 86
 
5.0%
u 73
 
4.2%
a 59
 
3.4%
Other values (22) 509
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1397
80.7%
Space Separator 151
 
8.7%
Uppercase Letter 91
 
5.3%
Other Punctuation 44
 
2.5%
Close Punctuation 24
 
1.4%
Open Punctuation 24
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 163
11.7%
o 162
11.6%
e 159
11.4%
n 144
10.3%
i 129
9.2%
s 96
 
6.9%
r 86
 
6.2%
u 73
 
5.2%
a 59
 
4.2%
c 50
 
3.6%
Other values (10) 276
19.8%
Uppercase Letter
ValueCountFrequency (%)
C 24
26.4%
O 18
19.8%
I 14
15.4%
D 14
15.4%
A 13
14.3%
M 7
 
7.7%
N 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
; 40
90.9%
/ 4
 
9.1%
Space Separator
ValueCountFrequency (%)
151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1488
86.0%
Common 243
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 163
11.0%
o 162
10.9%
e 159
10.7%
n 144
 
9.7%
i 129
 
8.7%
s 96
 
6.5%
r 86
 
5.8%
u 73
 
4.9%
a 59
 
4.0%
c 50
 
3.4%
Other values (17) 367
24.7%
Common
ValueCountFrequency (%)
151
62.1%
; 40
 
16.5%
) 24
 
9.9%
( 24
 
9.9%
/ 4
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 163
 
9.4%
o 162
 
9.4%
e 159
 
9.2%
151
 
8.7%
n 144
 
8.3%
i 129
 
7.5%
s 96
 
5.5%
r 86
 
5.0%
u 73
 
4.2%
a 59
 
3.4%
Other values (22) 509
29.4%

TrueFalse_1
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)11.8%
Missing33
Missing (%)66.0%
Memory size228.0 B
True
11 
False
(Missing)
33 
ValueCountFrequency (%)
True 11
 
22.0%
False 6
 
12.0%
(Missing) 33
66.0%
2023-05-21T23:36:48.093087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

TrueFalse_2
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)11.1%
Missing32
Missing (%)64.0%
Memory size228.0 B
True
11 
False
(Missing)
32 
ValueCountFrequency (%)
True 11
 
22.0%
False 7
 
14.0%
(Missing) 32
64.0%
2023-05-21T23:36:48.422881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

TrueFalse_3
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)11.1%
Missing32
Missing (%)64.0%
Memory size228.0 B
True
13 
False
(Missing)
32 
ValueCountFrequency (%)
True 13
26.0%
False 5
 
10.0%
(Missing) 32
64.0%
2023-05-21T23:36:48.722661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

SurveyLength
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)6.4%
Missing3
Missing (%)6.0%
Memory size528.0 B
Appropriate in length
37 
Too long
Too short

Length

Max length21
Median length21
Mean length18.319149
Min length8

Characters and Unicode

Total characters861
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowToo long
2nd rowAppropriate in length
3rd rowAppropriate in length
4th rowToo long
5th rowAppropriate in length

Common Values

ValueCountFrequency (%)
Appropriate in length 37
74.0%
Too long 6
 
12.0%
Too short 4
 
8.0%
(Missing) 3
 
6.0%

Length

2023-05-21T23:36:48.964771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:49.203914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
appropriate 37
28.2%
in 37
28.2%
length 37
28.2%
too 10
 
7.6%
long 6
 
4.6%
short 4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
p 111
12.9%
84
9.8%
n 80
9.3%
r 78
9.1%
t 78
9.1%
i 74
8.6%
e 74
8.6%
o 67
7.8%
l 43
 
5.0%
g 43
 
5.0%
Other values (5) 129
15.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 730
84.8%
Space Separator 84
 
9.8%
Uppercase Letter 47
 
5.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 111
15.2%
n 80
11.0%
r 78
10.7%
t 78
10.7%
i 74
10.1%
e 74
10.1%
o 67
9.2%
l 43
 
5.9%
g 43
 
5.9%
h 41
 
5.6%
Other values (2) 41
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
A 37
78.7%
T 10
 
21.3%
Space Separator
ValueCountFrequency (%)
84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 777
90.2%
Common 84
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 111
14.3%
n 80
10.3%
r 78
10.0%
t 78
10.0%
i 74
9.5%
e 74
9.5%
o 67
8.6%
l 43
 
5.5%
g 43
 
5.5%
h 41
 
5.3%
Other values (4) 88
11.3%
Common
ValueCountFrequency (%)
84
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 111
12.9%
84
9.8%
n 80
9.3%
r 78
9.1%
t 78
9.1%
i 74
8.6%
e 74
8.6%
o 67
7.8%
l 43
 
5.0%
g 43
 
5.0%
Other values (5) 129
15.0%

SurveyEase
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)6.2%
Missing2
Missing (%)4.0%
Memory size528.0 B
Easy
35 
Neither easy nor difficult
12 
Difficult
 
1

Length

Max length26
Median length4
Mean length9.6041667
Min length4

Characters and Unicode

Total characters461
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st rowDifficult
2nd rowNeither easy nor difficult
3rd rowEasy
4th rowEasy
5th rowEasy

Common Values

ValueCountFrequency (%)
Easy 35
70.0%
Neither easy nor difficult 12
 
24.0%
Difficult 1
 
2.0%
(Missing) 2
 
4.0%

Length

2023-05-21T23:36:49.402855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-21T23:36:49.626176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
easy 47
56.0%
difficult 13
 
15.5%
neither 12
 
14.3%
nor 12
 
14.3%

Most occurring characters

ValueCountFrequency (%)
a 47
10.2%
s 47
10.2%
y 47
10.2%
i 38
 
8.2%
e 36
 
7.8%
36
 
7.8%
E 35
 
7.6%
f 26
 
5.6%
t 25
 
5.4%
r 24
 
5.2%
Other values (9) 100
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 377
81.8%
Uppercase Letter 48
 
10.4%
Space Separator 36
 
7.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 47
12.5%
s 47
12.5%
y 47
12.5%
i 38
10.1%
e 36
9.5%
f 26
6.9%
t 25
6.6%
r 24
 
6.4%
c 13
 
3.4%
u 13
 
3.4%
Other values (5) 61
16.2%
Uppercase Letter
ValueCountFrequency (%)
E 35
72.9%
N 12
 
25.0%
D 1
 
2.1%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 425
92.2%
Common 36
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 47
11.1%
s 47
11.1%
y 47
11.1%
i 38
8.9%
e 36
8.5%
E 35
8.2%
f 26
 
6.1%
t 25
 
5.9%
r 24
 
5.6%
c 13
 
3.1%
Other values (8) 87
20.5%
Common
ValueCountFrequency (%)
36
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 47
10.2%
s 47
10.2%
y 47
10.2%
i 38
 
8.2%
e 36
 
7.8%
36
 
7.8%
E 35
 
7.6%
f 26
 
5.6%
t 25
 
5.4%
r 24
 
5.2%
Other values (9) 100
21.7%

ConvertedCompYearly
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)91.7%
Missing26
Missing (%)52.0%
Infinite0
Infinite (%)0.0%
Mean158816.54
Minimum5124
Maximum1663644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-05-21T23:36:49.836371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5124
5-th percentile21459.3
Q151192
median94126
Q3130000
95-th percentile213340.65
Maximum1663644
Range1658520
Interquartile range (IQR)78808

Descriptive statistics

Standard deviation325234.95
Coefficient of variation (CV)2.0478657
Kurtosis22.454284
Mean158816.54
Median Absolute Deviation (MAD)38374
Skewness4.673894
Sum3811597
Variance1.0577777 × 1011
MonotonicityNot monotonic
2023-05-21T23:36:50.152860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
130000 2
 
4.0%
51192 2
 
4.0%
97605 1
 
2.0%
135000 1
 
2.0%
5124 1
 
2.0%
68160 1
 
2.0%
83256 1
 
2.0%
102000 1
 
2.0%
1663644 1
 
2.0%
106960 1
 
2.0%
Other values (12) 12
24.0%
(Missing) 26
52.0%
ValueCountFrequency (%)
5124 1
2.0%
19224 1
2.0%
34126 1
2.0%
40205 1
2.0%
49056 1
2.0%
51192 2
4.0%
60307 1
2.0%
65000 1
2.0%
68160 1
2.0%
83256 1
2.0%
ValueCountFrequency (%)
1663644 1
2.0%
215232 1
2.0%
202623 1
2.0%
194400 1
2.0%
135000 1
2.0%
130000 2
4.0%
110000 1
2.0%
106960 1
2.0%
106644 1
2.0%
102000 1
2.0%

Interactions

2023-05-21T23:35:57.642685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:49.212850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:50.481223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:52.085378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:54.087562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:55.888735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:57.876135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:49.407318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:50.706295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:52.296838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:54.356482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:56.247085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:58.115321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:49.614933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:51.037255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:52.532983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:54.712772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:56.609307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:58.336935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:49.819146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:51.372963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:53.113201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:54.993661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:56.814704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:58.584329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:50.042697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:51.659613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:53.574979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:55.329789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:57.195860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:58.798089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:50.236109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:51.862873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:53.836175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:55.562618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-21T23:35:57.406694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-21T23:36:51.728399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ResponseIdYearsCodeYearsCodeProCompTotalWorkExpConvertedCompYearlyMainBranchEmploymentRemoteWorkCodingActivitiesEdLevelLearnCodeLearnCodeOnlineLearnCodeCoursesCertDevTypeOrgSizePurchaseInfluenceBuyNewToolCountryCurrencyCompFreqLanguageHaveWorkedWithLanguageWantToWorkWithDatabaseHaveWorkedWithDatabaseWantToWorkWithPlatformHaveWorkedWithPlatformWantToWorkWithWebframeHaveWorkedWithWebframeWantToWorkWithMiscTechHaveWorkedWithMiscTechWantToWorkWithToolsTechHaveWorkedWithToolsTechWantToWorkWithNEWCollabToolsHaveWorkedWithNEWCollabToolsWantToWorkWithOpSysProfessional useOpSysPersonal useVersionControlSystemVCInteractionOfficeStackAsyncHaveWorkedWithOfficeStackAsyncWantToWorkWithOfficeStackSyncHaveWorkedWithOfficeStackSyncWantToWorkWithBlockchainNEWSOSitesSOVisitFreqSOAccountSOPartFreqSOCommAgeGenderTransSexualityEthnicityAccessibilityMentalHealthTBranchICorPMKnowledge_1Knowledge_2Knowledge_3Knowledge_4Knowledge_5Knowledge_6Knowledge_7Frequency_1Frequency_2Frequency_3TimeSearchingTimeAnsweringOnboardingProfessionalTechTrueFalse_1TrueFalse_2TrueFalse_3SurveyLengthSurveyEase
ResponseId1.000-0.0220.066-0.048-0.0780.0730.0000.0000.1150.0000.0000.0000.2410.2700.1190.0000.0000.0000.1340.1640.0000.0000.0000.0000.0000.3110.0000.0000.3350.0000.3610.0000.1990.0000.0000.1490.0000.2350.1730.0000.0000.1740.0000.1160.2750.0000.0000.0000.2170.0000.0960.0000.0860.1450.2520.0420.5100.0000.0000.4780.0000.0000.0000.2150.2130.2660.0000.0000.0000.1490.0000.0000.0000.0000.3670.1630.177
YearsCode-0.0221.0000.9390.4130.9060.4720.0000.0000.0000.1850.2370.1150.0000.0000.0000.0000.2780.0000.0000.1020.1321.0000.0000.0000.0000.0000.3750.0000.2490.0000.0000.0000.4070.0000.1130.0000.0000.4740.4220.2040.1820.1810.0000.0000.0000.2020.0000.0620.0990.5260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.0000.0000.0000.0000.2500.0000.4120.0000.1920.0000.0000.3440.055
YearsCodePro0.0660.9391.0000.4890.9200.5780.0000.2050.0000.2310.3240.0000.0000.0000.0000.0000.1010.2480.2620.1900.0001.0000.0000.0680.0000.0000.5251.0001.0000.0000.0000.3750.2450.0000.3740.0000.1560.2880.3500.0000.2420.1710.1410.0200.2380.4560.0000.0000.2060.6850.1961.0000.0000.0000.0000.0000.0000.8560.2090.0000.0000.0000.3420.0000.1740.4290.0000.2450.2750.0000.3900.0000.2410.0000.5320.2890.000
CompTotal-0.0480.4130.4891.0000.5480.8280.1200.0000.0000.0000.0000.0000.2111.0000.1770.0000.3650.0670.0000.0000.5261.0000.2960.0000.0000.0000.1831.0001.0000.0000.0000.4410.3550.0000.3640.0000.0000.0000.0000.3820.4080.0000.0000.0000.0770.2920.1010.2490.2790.1840.1021.0000.3270.0000.4080.0000.0000.7340.0000.0000.4870.3390.2660.0000.0000.6790.4630.4470.0000.5200.3090.0000.2840.0000.1890.1620.232
WorkExp-0.0780.9060.9200.5481.0000.5720.0000.0000.1460.0000.3720.5590.0000.8160.3950.0000.0000.5010.0000.0000.0001.0000.5770.0000.0000.0000.7071.0001.0000.3330.0000.5770.4560.0980.4460.2270.4551.0000.0000.0000.2460.0000.0000.2320.0000.0000.0000.0000.1220.4830.0001.0000.1440.0000.0000.0001.0000.2110.0000.0000.0000.2980.2180.0000.0000.2540.0000.0000.0000.2210.3100.0000.0000.0000.5490.4070.320
ConvertedCompYearly0.0730.4720.5780.8280.5721.0000.1810.3420.4490.6310.0000.1950.0001.0000.0000.4990.3800.0000.4220.0000.0241.0000.0000.4990.3080.0000.7071.0001.0000.0000.0000.4080.3640.0000.3430.2930.0600.0000.1270.5630.1530.3150.3560.0000.2540.0000.0000.0000.0000.4140.0001.0000.0000.0000.0000.0000.0240.9610.1660.0000.0000.0000.0000.0000.0000.6010.0000.3760.1460.0000.2540.3920.0000.0000.2950.4390.000
MainBranch0.0000.0000.0000.1200.0000.1811.0000.3920.5760.0000.2890.2000.2540.4710.3570.0000.0000.4940.4800.0000.0000.2610.0000.0000.0000.0000.2410.1530.2770.6260.3580.1790.3170.3320.0000.4950.2270.0000.3350.0000.7070.3990.3860.0000.0490.0000.3620.0820.1030.3410.2920.2960.4210.0000.0000.2790.1060.4100.1940.0000.0000.0000.0000.0000.0000.9350.2500.6120.0000.2500.5000.4470.0000.0000.0000.0000.000
Employment0.0000.0000.2050.0000.0000.3420.3921.0000.6740.2830.4030.0000.2970.6120.0000.3510.3010.3040.3850.6130.4410.4520.0000.5670.4740.2240.0000.0000.0000.0000.5590.0000.0000.0000.0000.0000.0000.0000.0000.6650.6270.3830.3300.3390.0000.1220.0000.2590.1640.3750.1580.0820.5460.2750.2180.3550.2030.3330.0000.2750.0000.0140.1260.0000.0000.1890.0000.0000.4640.0000.4620.0000.0000.2690.0000.1590.000
RemoteWork0.1150.0000.0000.0000.1460.4490.5760.6741.0000.0000.3900.1320.2830.1490.2140.4730.2120.5630.2370.3670.0000.0000.2460.1310.0000.3240.0000.0001.0000.0000.2130.0000.0000.0000.0000.1350.0000.0000.0000.0000.0000.5190.5450.3960.0000.2090.1720.0000.1190.3670.3830.5140.3980.2590.0000.2340.2620.0000.0000.4970.0000.0000.0000.0000.0000.0000.0000.6530.0000.4530.0000.0000.0000.0000.0000.2010.000
CodingActivities0.0000.1850.2310.0000.0000.6310.0000.2830.0001.0000.0240.0000.0000.2740.2450.3380.0000.0000.5140.2130.0710.0000.0000.3810.3540.0000.2220.5401.0000.3270.0000.1910.0000.0000.0000.3250.0000.0000.1670.4770.5730.3720.0000.0000.0000.0000.0000.1430.1530.0710.0000.0000.0000.2800.0000.0000.0000.7750.2050.2160.0000.1780.4180.1790.0000.4080.0000.0200.3490.0000.0000.4630.0000.2490.0000.4640.000
EdLevel0.0000.2370.3240.0000.3720.0000.2890.4030.3900.0241.0000.0000.2870.2040.2610.0000.0000.3920.0000.2500.0001.0000.0000.0000.2620.3480.0000.2080.0000.2080.0000.0000.0000.0000.2120.2180.0000.0000.0000.4560.3340.2450.0000.2310.0000.2490.3080.2240.0000.3160.0000.0000.1620.2030.0000.0000.0000.0000.3090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1470.0000.0000.0000.000
LearnCode0.0000.1150.0000.0000.5590.1950.2000.0000.1320.0000.0001.0000.0001.0000.0670.1580.0000.0000.4040.0000.0001.0000.0790.1920.0900.0000.3600.0000.3400.0000.0000.3870.3350.0000.2070.2210.0000.3600.0000.0000.0000.0000.0000.1630.1670.2010.2880.0000.2430.0000.1270.0000.3530.0000.2740.2950.0000.2580.3540.0730.2350.0000.0000.0000.4330.4330.1180.3540.0000.2250.0000.3940.0000.0000.2500.0000.401
LearnCodeOnline0.2410.0000.0000.2110.0000.0000.2540.2970.2830.0000.2870.0001.0001.0000.3830.1670.0000.0000.0000.5770.2501.0000.4350.3920.3871.0001.0000.0001.0001.0001.0000.2090.0000.0001.0000.1470.0000.3110.4830.0001.0000.3281.0000.4080.0000.0910.2540.1780.0000.0000.3160.1830.2300.3710.1891.0000.2240.3330.0000.2290.5480.5480.0820.5480.5480.0000.5480.4470.0000.5480.5480.0000.0000.0000.3160.0000.180
LearnCodeCoursesCert0.2700.0000.0001.0000.8161.0000.4710.6120.1490.2740.2041.0001.0001.0001.0000.5770.0000.2700.9430.5770.4471.0001.0000.2360.0000.0000.3540.0001.0001.0000.2501.0000.2310.0000.0000.0000.4081.0000.1021.0001.0000.4880.3160.3950.0000.0000.4710.0000.0000.0000.5001.0000.2340.0001.0000.0000.5771.0000.0000.0000.8160.8160.0000.0000.0000.5770.5770.0000.0000.8160.0001.0000.0000.5771.0000.0000.471
DevType0.1190.0000.0000.1770.3950.0000.3570.0000.2140.2450.2610.0670.3831.0001.0000.1380.3030.4820.0000.0000.0001.0000.8040.2880.0000.3510.6250.0001.0000.4880.0000.1840.5120.0000.2770.2740.4320.3870.0000.1670.3320.3070.3650.1020.2750.0000.0000.0000.0000.0000.4320.6320.0000.4270.0000.0000.0000.5770.0000.0000.0000.0000.0000.1540.0000.0000.0000.0000.3490.0000.4050.0000.0000.0000.0000.0000.254
OrgSize0.0000.0000.0000.0000.0000.4990.0000.3510.4730.3380.0000.1580.1670.5770.1381.0000.4600.0000.1590.0000.3631.0000.0000.0550.4710.0000.0001.0001.0000.2390.3120.3200.0000.1810.0000.0000.0000.0000.0000.1940.1890.0000.2840.2100.2790.0000.0000.0000.1990.0000.5921.0000.5610.2950.8660.3330.0000.0000.4400.0000.3600.2980.3630.0000.0000.3980.4690.0000.3690.3940.0000.7160.0000.0000.0000.0000.000
PurchaseInfluence0.0000.2780.1010.3650.0000.3800.0000.3010.2120.0000.0000.0000.0000.0000.3030.4601.0000.3790.0790.0000.1851.0000.0000.1200.2570.0000.0001.0001.0000.0340.3260.0000.2650.0000.0000.0000.0000.2980.2720.4800.0000.0000.1890.2290.0000.1150.2120.0000.0320.2310.0001.0000.1860.0000.1090.3270.0000.0000.0520.0000.3890.0000.0000.2540.0880.0000.2780.0820.0000.0850.0000.4630.0000.4660.0000.0000.000
BuyNewTool0.0000.0000.2480.0670.5010.0000.4940.3040.5630.0000.3920.0000.0000.2700.4820.0000.3791.0000.0000.0000.0001.0000.1390.3410.0000.0000.0000.1950.0000.5370.4050.0000.2810.0000.1090.0000.1470.2540.0000.0000.0000.3010.3810.0000.0000.0000.1090.1190.3790.1910.0000.0000.4420.0000.0000.3240.0000.0000.1130.3420.3030.0000.2860.2770.5010.0000.0000.0810.0000.0000.0000.0000.0000.3800.3940.0000.338
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PlatformWantToWorkWith0.0000.3750.5250.1830.7070.7070.2410.0000.0000.2220.0000.3601.0000.3540.6250.0000.0000.0000.6170.5460.3291.0001.0000.7171.0000.3571.0000.0001.0000.0001.0000.3950.3910.0000.3260.5650.3170.1600.0000.1860.0000.6060.5190.0000.4980.3270.0000.2820.0000.0000.5941.0000.0000.3741.0000.0000.0000.6320.0000.2890.0000.0000.0000.2890.0000.0000.0000.2891.0000.0000.3541.0000.0000.0000.6320.3340.000
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WebframeWantToWorkWith0.3350.2491.0001.0001.0001.0000.2770.0001.0001.0000.0000.3401.0001.0001.0001.0001.0000.0000.5631.0001.0001.0000.0000.1700.0000.5591.0000.0001.0000.1651.0000.1261.0000.0000.4750.3900.2130.2770.5190.2100.0000.4610.2850.0000.3400.1280.0001.0000.3920.0000.2831.0000.0000.0000.2040.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.2830.196
MiscTechHaveWorkedWith0.0000.0000.0000.0000.3330.0000.6260.0000.0000.3270.2080.0001.0001.0000.4880.2390.0340.5370.0000.0000.0001.0000.8080.3670.3290.6570.0000.2700.1651.0000.5550.4380.3810.6590.0000.4650.4300.0000.0000.2610.2580.1080.0000.0000.2790.3710.6260.0000.0000.0001.0001.0000.0000.0001.0000.0000.0000.4470.0000.0000.0000.4080.3730.0000.0000.5480.4800.3510.0000.3330.0000.3350.0000.0000.0000.3260.438
MiscTechWantToWorkWith0.3610.0000.0000.0000.0000.0000.3580.5590.2130.0000.0000.0001.0000.2500.0000.3120.3260.4050.0000.0000.0001.0001.0000.4250.7161.0001.0000.3581.0000.5551.0000.0000.0001.0000.0000.4330.4770.0000.0000.5001.0000.0000.2100.2360.0000.0000.5420.0000.0770.0001.0001.0000.0000.0001.0000.4270.0001.0000.5000.2170.0000.0000.0000.2170.0000.5000.2800.5000.0000.0000.0000.0000.0000.0000.3540.0000.000
ToolsTechHaveWorkedWith0.0000.0000.3750.4410.5770.4080.1790.0000.0000.1910.0000.3870.2091.0000.1840.3200.0000.0000.0000.0000.0001.0001.0000.3290.2550.0000.3950.4120.1260.4380.0001.0000.4040.0000.2040.0000.0000.0000.2950.0000.0000.0900.0000.0000.0000.0000.0000.0660.0000.0000.3750.0000.0000.3200.3460.0000.0000.2890.0000.0000.0000.0000.0790.5000.0000.0000.0000.4080.5000.0000.5771.0001.0000.0000.2890.0000.000
ToolsTechWantToWorkWith0.1990.4070.2450.3550.4560.3640.3170.0000.0000.0000.0000.3350.0000.2310.5120.0000.2650.2810.0000.2920.3561.0001.0000.1690.0000.0000.3910.0001.0000.3810.0000.4041.0000.0000.1590.0000.3180.3180.2790.0000.1770.3000.1960.0000.2370.3230.0000.0000.0000.0000.6740.6590.2360.0000.3070.4550.0000.6030.0000.0000.0000.0000.0000.0000.0000.4710.0000.0000.0000.0000.4210.0000.3340.0000.0000.3330.000
NEWCollabToolsHaveWorkedWith0.0000.0000.0000.0000.0980.0000.3320.0000.0000.0000.0000.0000.0000.0000.0000.1810.0000.0000.2920.1870.2821.0000.0000.0000.0000.0000.0000.0000.0000.6591.0000.0000.0001.0000.6470.4000.1440.0000.2110.2550.3460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0660.0000.0000.4470.0000.1040.3650.2200.0410.0000.1860.2420.0000.0000.4470.3750.2260.0000.4010.0000.0000.0000.0280.0000.0000.264
NEWCollabToolsWantToWorkWith0.0000.1130.3740.3640.4460.3430.0000.0000.0000.0000.2120.2071.0000.0000.2770.0000.0000.1090.0000.0000.0001.0000.0000.0000.0000.0000.3260.0000.4750.0000.0000.2040.1590.6471.0000.2560.3860.1420.0880.0000.0000.0000.0000.0000.4130.0000.0000.0840.0000.1730.0000.0000.0000.0000.2030.0000.3690.5220.3040.0000.2670.3630.2170.0000.2750.4510.2260.1910.0000.3610.0000.0000.4260.2090.0000.0000.324
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VCInteraction0.1730.4220.3500.0000.0000.1270.3350.0000.0000.1670.0000.0000.4830.1020.0000.0000.2720.0000.1990.0000.0001.0000.0000.0000.0000.1930.0000.3690.5190.0000.0000.2950.2790.2110.0880.4880.0000.4481.0000.3210.1550.3940.1530.0000.0000.0000.1940.0000.0610.2250.4740.6100.0000.0000.3440.0000.0000.7750.1250.3840.0000.0000.0000.2990.1050.4690.2010.4410.0000.2080.1290.4170.0690.3690.3090.0000.000
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OfficeStackAsyncWantToWorkWith0.0000.1820.2420.4080.2460.1530.7070.6270.0000.5730.3340.0001.0001.0000.3320.1890.0000.0000.0000.0000.0001.0001.0000.0001.0001.0000.0001.0000.0000.2581.0000.0000.1770.3460.0000.2880.2900.0000.1550.5941.0000.0820.0000.3230.0000.0000.1310.0000.1030.2940.0001.0000.1780.0000.0000.0000.0000.5000.5350.2310.0000.4010.4910.0000.0000.2310.0000.2460.0000.0000.2930.3780.3780.0000.1250.5800.000
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OfficeStackSyncWantToWorkWith0.0000.0000.1410.0000.0000.3560.3860.3300.5450.0000.0000.0001.0000.3160.3650.2840.1890.3810.0000.0000.0001.0001.0000.0000.0000.0000.5190.3860.2850.0000.2100.0000.1960.0000.0000.1900.0000.4400.1530.0000.0000.6601.0000.1600.1310.0000.0000.0900.0000.4260.7090.8660.3680.5560.2430.2810.0000.0000.0000.3610.5640.3650.0000.0000.4720.0000.2120.0000.4350.1700.5070.0000.6130.0000.0000.0000.075
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TBranch0.5100.0000.0000.0001.0000.0240.1060.2030.2620.0000.0000.0000.2240.5770.0000.0000.0000.0000.2890.1680.0000.0000.1960.3370.4590.0000.0001.0001.0000.0000.0000.0000.0000.1040.3690.0000.4730.2240.0000.1270.0000.0000.0000.3060.2160.0000.1050.0000.0000.0000.0001.0000.1210.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.233
ICorPM0.0000.0000.8560.7340.2110.9610.4100.3330.0000.7750.0000.2580.3331.0000.5770.0000.0000.0000.0000.0000.0001.0000.2670.3920.3160.0000.6321.0001.0000.4471.0000.2890.6030.3650.5220.8320.8941.0000.7750.0000.5000.7070.0000.5480.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.8861.0001.0000.2890.0000.0000.0000.0000.0000.0000.9310.2110.6060.0000.2110.8940.3780.0000.0000.0000.6060.000
Knowledge_10.0000.0000.2090.0000.0000.1660.1940.0000.0000.2050.3090.3540.0000.0000.0000.4400.0520.1130.0000.0000.0001.0000.0000.0000.4470.0000.0001.0001.0000.0000.5000.0000.0000.2200.3040.2450.2251.0000.1250.0000.5350.0000.0000.0000.3180.0000.2260.2760.3050.0000.0001.0000.2380.0000.0000.0001.0000.2891.0000.0000.1220.0000.2800.0000.0000.3270.0000.0000.1340.0000.0380.0000.0000.4660.0000.0000.000
Knowledge_20.4780.0000.0000.0000.0000.0000.0000.2750.4970.2160.0000.0730.2290.0000.0000.0000.0000.3420.0000.0880.0001.0000.0000.0000.0000.3230.2891.0001.0000.0000.2170.0000.0000.0410.0000.0000.0001.0000.3840.3640.2310.2940.3610.0000.0000.0000.0000.0000.4400.0990.0001.0000.0000.1610.1770.0001.0000.0000.0001.0000.2580.0000.0000.3590.5070.0000.1730.3010.0000.3240.0000.2340.2290.5130.0000.0000.000
Knowledge_30.0000.0000.0000.4870.0000.0000.0000.0000.0000.0000.0000.2350.5480.8160.0000.3600.3890.3030.0000.0000.2311.0000.0000.0000.5480.0000.0001.0001.0000.0000.0000.0000.0000.0000.2670.0000.1461.0000.0000.3620.0000.0960.5640.3210.0000.0000.6940.4910.1630.2800.0001.0000.0000.0000.0000.2451.0000.0000.1220.2581.0000.4990.4690.0000.6110.0000.3970.0000.0000.3160.0000.3490.0000.2900.2590.0000.000
Knowledge_40.0000.0000.0000.3390.2980.0000.0000.0140.0000.1780.0000.0000.5480.8160.0000.2980.0000.0000.0000.0000.5351.0000.0000.0000.2290.0000.0001.0001.0000.4080.0000.0000.0000.1860.3630.3430.4931.0000.0000.0000.4010.0000.3650.3150.0000.0000.0770.1780.0000.1040.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.4991.0000.7260.0000.1430.0000.0000.0000.1680.1230.1260.0000.0000.1350.0000.0000.000
Knowledge_50.0000.0000.3420.2660.2180.0000.0000.1260.0000.4180.0000.0000.0820.0000.0000.3630.0000.2860.0000.0000.4991.0000.0000.0000.1580.0000.0001.0001.0000.3730.0000.0790.0000.2420.2170.3850.4571.0000.0000.2890.4910.0000.0000.2530.0000.0000.2280.3710.0000.3220.0001.0000.0000.0000.0000.0001.0000.0000.2800.0000.4690.7261.0000.0000.0000.3990.0000.0000.0140.1390.0000.0000.2060.4380.2030.0000.000
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TimeSearching0.0000.2500.2750.0000.0000.1460.0000.4640.0000.3490.0000.0000.0000.0000.3490.3690.0000.0000.6790.4750.1401.0000.0000.2600.5480.0001.0001.0001.0000.0000.0000.5000.0000.0000.0000.2980.0001.0000.0000.4020.0000.0000.4350.1700.3190.3270.0000.4980.2600.0000.4461.0000.0000.0880.3540.1721.0000.0000.1340.0000.0000.1680.0140.0000.0000.0000.0000.0971.0000.0000.6080.0000.3660.0000.2450.4440.000
TimeAnswering0.1490.0000.0000.5200.2210.0000.2500.0000.4530.0000.0000.2250.5480.8160.0000.3940.0850.0000.1270.0000.2091.0000.4470.0000.0000.5770.0001.0001.0000.3330.0000.0000.0000.4010.3610.2700.3621.0000.2080.0000.0000.0000.1700.0000.4650.0940.2060.0000.3890.0000.6221.0000.5830.0000.9010.3851.0000.2110.0000.3240.3160.1230.1390.0000.2220.5340.3680.6290.0001.0000.0000.0000.2330.2970.0000.0000.000
Onboarding0.0000.4120.3900.3090.3100.2540.5000.4620.0000.0000.0000.0000.5480.0000.4050.0000.0000.0000.0000.2380.0001.0000.0000.1680.3540.0000.3541.0001.0000.0000.0000.5770.4210.0000.0000.0000.4591.0000.1290.0000.2930.2870.5070.0000.0000.1040.0000.5290.0000.1630.3891.0000.0000.0000.0000.2981.0000.8940.0380.0000.0000.1260.0000.2380.0000.0000.1530.0000.6080.0001.0000.0000.5010.0000.2770.4790.099
ProfessionalTech0.0000.0000.0000.0000.0000.3920.4470.0000.0000.4630.0000.3940.0001.0000.0000.7160.4630.0000.0000.0000.2771.0000.0000.0960.4080.0001.0001.0001.0000.3350.0001.0000.0000.0000.0000.2850.0001.0000.4170.6120.3780.3820.0000.4050.3350.0000.4630.2320.2260.0000.0001.0000.4630.0000.4470.4111.0000.3780.0000.2340.3490.0000.0000.3250.3890.4800.2220.2910.0000.0000.0001.0000.0000.1650.0000.0000.447
TrueFalse_10.0000.1920.2410.2840.0000.0000.0000.0000.0000.0000.1470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.3330.0630.0001.0001.0000.0000.0001.0000.3340.0000.4260.0000.0571.0000.0690.3790.3780.1020.6130.0000.4820.0000.0000.5420.0000.0000.0001.0000.1320.2570.0000.3251.0000.0000.0000.2290.0000.0000.2060.0000.0000.0000.0000.0000.3660.2330.5010.0001.0000.0000.0000.0000.000
TrueFalse_20.0000.0000.0000.0000.0000.0000.0000.2690.0000.2490.0000.0000.0000.5770.0000.0000.4660.3800.4110.0000.0001.0000.2580.1010.0000.1070.0001.0001.0000.0000.0000.0000.0000.0280.2090.0000.0001.0000.3690.6200.0000.0000.0000.0000.0000.0000.2080.0000.3770.0000.0001.0000.0780.1270.0000.0001.0000.0000.4660.5130.2900.1350.4380.0000.0750.0000.0000.0000.0000.2970.0000.1650.0001.0000.0000.0000.000
TrueFalse_30.3670.0000.5320.1890.5490.2950.0000.0000.0000.0000.0000.2500.3161.0000.0000.0000.0000.3940.0000.0000.0001.0000.2580.4630.3160.0000.6321.0001.0000.0000.3540.2890.0000.0000.0000.3910.0001.0000.3090.0000.1250.0000.0000.5690.0000.3370.0000.1390.0000.7400.0001.0000.0000.0000.0000.0001.0000.0000.0000.0000.2590.0000.2030.0000.5690.2450.1070.5440.2450.0000.2770.0000.0000.0001.0000.4760.000
SurveyLength0.1630.3440.2890.1620.4070.4390.0000.1590.2010.4640.0000.0000.0000.0000.0000.0000.0000.0000.4160.3560.2450.2160.0000.4910.0000.0000.3340.3220.2830.3260.0000.0000.3330.0000.0000.0000.0000.2070.0000.4400.5800.4040.0000.0000.1220.2680.0000.0000.1700.0000.0000.0780.0000.0000.0000.1720.0000.6060.0000.0000.0000.0000.0000.0000.0000.2970.4760.0000.4440.0000.4790.0000.0000.0000.4761.0000.181
SurveyEase0.1770.0550.0000.2320.3200.0000.0000.0000.0000.0000.0000.4010.1800.4710.2540.0000.0000.3380.0000.3360.2340.0000.1260.0000.0000.2460.0000.0000.1960.4380.0000.0000.0000.2640.3240.0000.6320.0000.0000.0000.0000.2470.0750.0000.1910.0000.0000.1370.7080.0000.0000.0000.0000.0000.0000.1930.2330.0000.0000.0000.0000.0000.0000.4620.0000.0000.0000.0000.0000.0000.0990.4470.0000.0000.0000.1811.000

Missing values

2023-05-21T23:35:59.574896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-21T23:36:01.568461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-21T23:36:04.906880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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23I am not primarily a developer, but I write code sometimes as part of my workEmployed, full-timeHybrid (some remote, some in-person)HobbyMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Books / Physical media;Friend or family member;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)Technical documentation;Blogs;Programming Games;Written Tutorials;Stack OverflowNaN14.05.0Data scientist or machine learning specialist;Developer, front-end;Engineer, data;Engineer, site reliability20 to 99 employeesI have some influenceNaNUnited Kingdom of Great Britain and Northern IrelandGBP\tPound sterling32000.0YearlyC#;C++;HTML/CSS;JavaScript;PythonC#;C++;HTML/CSS;JavaScript;TypeScriptMicrosoft SQL ServerMicrosoft SQL ServerNaNNaNAngular.jsAngular;Angular.jsPandas.NETNaNNaNNotepad++;Visual StudioNotepad++;Visual StudioWindowsWindowsGitCode editorNaNNaNNaNNaNMicrosoft TeamsMicrosoft TeamsVery unfavorableCollectives on Stack Overflow;Stack Overflow;Stack ExchangeMultiple times per dayYesMultiple times per dayNeutral25-34 years oldManNoBisexualWhiteNone of the aboveI have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.);I have an anxiety disorderNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthNeither easy nor difficult40205.0
34I am a developer by professionEmployed, full-timeFully remoteI don’t code outside of workBachelor’s degree (B.A., B.S., B.Eng., etc.)Books / Physical media;School (i.e., University, College, etc)NaNNaN20.017.0Developer, full-stack100 to 499 employeesI have some influenceOther (please specify):IsraelILS\tIsraeli new shekel60000.0MonthlyC#;JavaScript;SQL;TypeScriptC#;SQL;TypeScriptMicrosoft SQL ServerMicrosoft SQL ServerNaNNaNASP.NET;ASP.NET CoreASP.NET;ASP.NET Core.NET.NETNaNNaNNotepad++;Visual Studio;Visual Studio CodeNotepad++;Visual Studio;Visual Studio CodeWindowsWindowsGitCode editor;Command-line;Version control hosting service web GUI;Dedicated version control GUI applicationNaNNaNJira Work Management;TrelloJira Work Management;TrelloSlack;ZoomSlack;ZoomVery unfavorableCollectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack ExchangeDaily or almost dailyYesA few times per weekYes, definitely35-44 years oldManNoStraight / HeterosexualWhiteNone of the aboveNone of the aboveNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasy215232.0
45I am a developer by professionEmployed, full-timeHybrid (some remote, some in-person)HobbyBachelor’s degree (B.A., B.S., B.Eng., etc.)Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc);On the job trainingTechnical documentation;Blogs;Stack Overflow;Online books;Video-based Online Courses;Online challenges (e.g., daily or weekly coding challenges)NaN8.03.0Developer, front-end;Developer, full-stack;Developer, back-end;Developer, desktop or enterprise applications;Developer, QA or test20 to 99 employeesI have some influenceStart a free trial;Visit developer communities like Stack OverflowUnited States of AmericaUSD\tUnited States dollarNaNNaNC#;HTML/CSS;JavaScript;SQL;Swift;TypeScriptC#;Elixir;F#;Go;JavaScript;Rust;TypeScriptCloud Firestore;Elasticsearch;Microsoft SQL Server;Firebase Realtime DatabaseCloud Firestore;Elasticsearch;Firebase Realtime Database;RedisFirebase;Microsoft AzureFirebase;Microsoft AzureAngular;ASP.NET;ASP.NET Core ;jQuery;Node.jsAngular;ASP.NET Core ;Blazor;Node.js.NET.NET;Apache KafkanpmDocker;KubernetesNotepad++;Visual Studio;Visual Studio Code;XcodeRider;Visual Studio;Visual Studio CodeWindowsmacOS;WindowsGit;Other (please specify):Code editorNaNNaNNaNNaNMicrosoft Teams;ZoomNaNUnfavorableCollectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack ExchangeMultiple times per dayYesDaily or almost dailyYes, definitely25-34 years oldNaNNaNNaNNaNNaNNaNNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNToo longEasyNaN
56I am not primarily a developer, but I write code sometimes as part of my workStudent, full-timeNaNNaNMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)Books / Physical media;School (i.e., University, College, etc)NaNNaN15.0NaNNaNNaNNaNOther (please specify):GermanyNaNNaNNaNC++;LuaLuaNaNNaNNaNNaNNaNNaNNaNNaNHomebrewHomebrewVisual Studio Code;XcodeVisual Studio CodeLinux-based;macOSmacOSGitCommand-line;Version control hosting service web GUI;Dedicated version control GUI applicationNaNNaNConfluenceNaNRocketchat;Slack;ZoomRocketchat;Slack;ZoomVery unfavorableStack Overflow;Stack ExchangeMultiple times per dayYesMultiple times per dayYes, definitely25-34 years oldOr, in your own words:Or, in your own words:Prefer to self-describe:Or, in your own words:Or, in your own words:Or, in your own words:NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasyNaN
67I code primarily as a hobbyStudent, part-timeNaNNaNSecondary school (e.g. American high school, German Realschule or Gymnasium, etc.)Other online resources (e.g., videos, blogs, forum)Stack Overflow;Video-based Online CoursesNaN3.0NaNNaNNaNNaNStart a free trial;Visit developer communities like Stack OverflowIndiaNaNNaNNaNC++;HTML/CSS;JavaScript;PHP;Python;TypeScriptC;C#;C++;Elixir;Go;HTML/CSS;Java;JavaScript;Kotlin;Python;Rust;Swift;TypeScriptCloud Firestore;MongoDB;Firebase Realtime DatabaseMySQL;Oracle;PostgreSQLNaNNaNAngular;Next.js;Node.js;React.js;Svelte;Vue.jsDjango;Flask;Gatsby;jQuery;Next.js;Node.js;React.js;Svelte;Vue.jsNaNNaNHomebrew;npmnpmAtom;IntelliJ;Notepad++;PyCharm;Sublime Text;Visual Studio CodeVisual Studio Code;WebstormmacOSmacOSGitCode editor;Command-lineNaNNaNNaNNaNGoogle Chat;Microsoft Teams;Slack;ZoomGoogle Chat;Slack;ZoomFavorableStack OverflowMultiple times per dayYesDaily or almost dailyYes, definitelyUnder 18 years oldManNoNaNIndianNone of the aboveNone of the aboveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasyNaN
78I am a developer by professionNot employed, but looking for workNaNNaNSome college/university study without earning a degreeOnline Courses or CertificationNaNCoursera;Udemy1.0NaNDeveloper, full-stack;StudentNaNNaNStart a free trialIndiaNaNNaNNaNC;C++;HTML/CSS;Java;JavaScript;SQLAPL;Bash/Shell;Go;Python;TypeScriptMongoDB;MySQLNeo4j;PostgreSQLAWS;Google Cloud;HerokuDigitalOcean;Firebase;Microsoft Azure;VMwarejQuery;Node.jsAngular;Angular.js;Next.js;Vue.jsNaNNaNnpmUnity 3D;YarnAtom;CLion;Eclipse;IntelliJ;Notepad++;Visual StudioAndroid Studio;IPython/Jupyter;Sublime Text;Vim;Visual Studio CodeLinux-based;macOSWindowsGitCommand-lineNaNNaNNaNNaNGoogle Chat;Microsoft Teams;ZoomNaNVery favorableCollectives on Stack Overflow;Stack Overflow;Stack ExchangeA few times per weekYesI have never participated in Q&A on Stack OverflowYes, definitely18-24 years oldManNoStraight / HeterosexualIndianNone of the aboveNone of the aboveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasyNaN
89I am a developer by professionEmployed, full-timeHybrid (some remote, some in-person)I don’t code outside of workMaster’s degree (M.A., M.S., M.Eng., MBA, etc.)On the job training;Coding BootcampNaNNaN6.06.0Developer, back-endI don’t knowI have little or no influenceNaNNetherlandsEUR European Euro46000.0YearlyNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNEmacs;Notepad++Emacs;Notepad++WindowsWindowsGitCommand-line;Dedicated version control GUI applicationNaNNaNConfluence;Jira Work ManagementConfluence;Jira Work ManagementMicrosoft TeamsMicrosoft TeamsVery unfavorableStack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack ExchangeA few times per month or weeklyYesLess than once per month or monthlyNo, not at all25-34 years oldWomanNoPrefer to self-describe:EuropeanOr, in your own words:Or, in your own words:YesIndependent contributor6.0AgreeDisagreeAgreeAgreeAgreeAgreeDisagree3-5 times a week3-5 times a weekNever15-30 minutes a dayOver 120 minutes a daySomewhat longInnersource initiative;DevOps function;Microservices;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery;Automated testing;Observability toolsYesYesYesAppropriate in lengthEasy49056.0
910I am a developer by professionIndependent contractor, freelancer, or self-employedFully remoteHobby;Contribute to open-source projects;Bootstrapping a businessSome college/university study without earning a degreeBooks / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online books;Online forumNaN37.030.0Developer, desktop or enterprise applications;Developer, mobile;EducatorJust me - I am a freelancer, sole proprietor, etc.I have a great deal of influenceStart a free trial;Ask developers I know/work with;Research companies that have advertised on sites I visitCroatiaHRK\tCroatian kunaNaNNaNDelphi;Java;SwiftDelphi;Java;SwiftNaNNaNDigitalOcean;FirebaseDigitalOcean;FirebaseNaNNaNNaNNaNNaNNaNAndroid Studio;RAD Studio (Delphi, C++ Builder);Visual Studio Code;XcodeAndroid Studio;RAD Studio (Delphi, C++ Builder);Visual Studio Code;XcodeWindowsWindowsGitVersion control hosting service web GUI;Dedicated version control GUI applicationNaNNaNNaNNaNGoogle Chat;SlackGoogle Chat;SlackVery unfavorableCollectives on Stack Overflow;Stack Overflow;Stack ExchangeMultiple times per dayYesMultiple times per dayYes, definitely45-54 years oldWomanNoStraight / HeterosexualWhite;EuropeanNone of the aboveNone of the aboveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasyNaN
ResponseIdMainBranchEmploymentRemoteWorkCodingActivitiesEdLevelLearnCodeLearnCodeOnlineLearnCodeCoursesCertYearsCodeYearsCodeProDevTypeOrgSizePurchaseInfluenceBuyNewToolCountryCurrencyCompTotalCompFreqLanguageHaveWorkedWithLanguageWantToWorkWithDatabaseHaveWorkedWithDatabaseWantToWorkWithPlatformHaveWorkedWithPlatformWantToWorkWithWebframeHaveWorkedWithWebframeWantToWorkWithMiscTechHaveWorkedWithMiscTechWantToWorkWithToolsTechHaveWorkedWithToolsTechWantToWorkWithNEWCollabToolsHaveWorkedWithNEWCollabToolsWantToWorkWithOpSysProfessional useOpSysPersonal useVersionControlSystemVCInteractionVCHostingPersonal useVCHostingProfessional useOfficeStackAsyncHaveWorkedWithOfficeStackAsyncWantToWorkWithOfficeStackSyncHaveWorkedWithOfficeStackSyncWantToWorkWithBlockchainNEWSOSitesSOVisitFreqSOAccountSOPartFreqSOCommAgeGenderTransSexualityEthnicityAccessibilityMentalHealthTBranchICorPMWorkExpKnowledge_1Knowledge_2Knowledge_3Knowledge_4Knowledge_5Knowledge_6Knowledge_7Frequency_1Frequency_2Frequency_3TimeSearchingTimeAnsweringOnboardingProfessionalTechTrueFalse_1TrueFalse_2TrueFalse_3SurveyLengthSurveyEaseConvertedCompYearly
4041I am a developer by professionEmployed, full-timeHybrid (some remote, some in-person)Hobby;Freelance/contract workAssociate degree (A.A., A.S., etc.)Books / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online forum;How-to videosNaN20.015.0Developer, front-end;Engineer, data;Developer, full-stack;Developer, back-end;Developer, desktop or enterprise applications;Developer, QA or test;Developer, mobile;Database administrator;Developer, embedded applications or devices;Cloud infrastructure engineer;Data or business analyst;Designer100 to 499 employeesI have a great deal of influenceStart a free trial;Visit developer communities like Stack Overflow;Ask developers I know/work with;Research companies that have advertised on sites I visit;Read ratings or reviews on third party sites like G2CrowdUnited States of AmericaUSD\tUnited States dollar130000.0YearlyC#;C++;HTML/CSS;JavaScript;SQL;TypeScript;VBANaNMongoDB;MySQL;SQLiteNaNAWS;Managed Hosting;Microsoft AzureNaNASP.NET;ASP.NET Core ;jQueryNaN.NET;XamarinNaNNaNNaNNotepad++;Visual Studio;Visual Studio CodeNaNNaNmacOS;WindowsGitCode editorNaNNaNNaNNaNNaNNaNUnsureStack Overflow;Stack ExchangeA few times per weekYesA few times per month or weeklyYes, somewhat35-44 years oldManNoStraight / HeterosexualWhiteNaNNone of the aboveNoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasy130000.0
4142I am a developer by professionEmployed, full-timeHybrid (some remote, some in-person)I don’t code outside of workSecondary school (e.g. American high school, German Realschule or Gymnasium, etc.)Other online resources (e.g., videos, blogs, forum);On the job training;Online Courses or CertificationTechnical documentation;Blogs;Written Tutorials;Stack Overflow;Video-based Online Courses;Written-based Online CoursesCoursera;Udemy;Udacity3.03.0Data scientist or machine learning specialist;Engineer, data;Developer, back-end20 to 99 employeesI have little or no influenceVisit developer communities like Stack Overflow;Ask developers I know/work with;Read ratings or reviews on third party sites like G2CrowdIsraelILS\tIsraeli new shekel19000.0MonthlyBash/Shell;HTML/CSS;Python;SQLBash/Shell;Python;SQLPostgreSQLPostgreSQL;RedisAWS;Google CloudAWSNaNNaNKeras;NumPy;Pandas;Scikit-learn;TensorFlowKeras;NumPy;Pandas;Scikit-learn;TensorFlow;Hugging Face TransformersDockerDockerIPython/Jupyter;VimIPython/Jupyter;PyCharm;VimLinux-basedWindowsGitCommand-lineNaNNaNConfluence;Jira Work ManagementConfluence;Jira Work ManagementSlackSlackIndifferentStack Overflow;Stack ExchangeDaily or almost dailyNoNaNNo, not really18-24 years oldManNoStraight / HeterosexualMiddle EasternNone of the aboveI have an anxiety disorderYesIndependent contributor3.0AgreeNeither agree nor disagreeDisagreeAgreeAgreeAgreeNeither agree nor disagree1-2 times a week10+ times a weekNever30-60 minutes a day15-30 minutes a daySomewhat shortNaNNaNYesYesAppropriate in lengthNeither easy nor difficult68160.0
4243I am a developer by professionNot employed, but looking for workNaNNaNBachelor’s degree (B.A., B.S., B.Eng., etc.)Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc);Online Courses or Certification;ColleagueTechnical documentation;Blogs;Stack Overflow;Video-based Online Courses;How-to videos;Written-based Online Courses;Interactive tutorial;Coding sessions (live or recorded)Udemy;Pluralsight4.0NaNDeveloper, front-end;Developer, full-stack;Developer, back-end;Database administratorNaNNaNStart a free trial;Visit developer communities like Stack Overflow;Ask developers I know/work withIraqNaNNaNNaNC#;HTML/CSS;JavaScript;PHP;Ruby;SQLC#;HTML/CSS;JavaScript;Python;Ruby;SQL;TypeScriptMicrosoft SQL Server;MongoDB;MySQL;Oracle;PostgreSQLDynamoDB;Elasticsearch;Microsoft SQL Server;MongoDB;MySQL;PostgreSQL;RedisAWS;Microsoft AzureAWS;Heroku;Microsoft AzureASP.NET Core ;jQuery;Ruby on RailsAngular;Angular.js;ASP.NET Core ;Blazor;Django;Node.js;React.js;Ruby on Rails.NET.NET;React NativeDocker;npm;YarnDockerEclipse;IntelliJ;NetBeans;Visual Studio;Visual Studio CodeVisual Studio;Visual Studio CodeLinux-based;WindowsWindowsGitCode editor;Command-lineNaNNaNNaNNaNMicrosoft Teams;Slack;ZoomMicrosoft Teams;ZoomVery favorableStack Overflow;Stack ExchangeDaily or almost dailyYesI have never participated in Q&A on Stack OverflowNeutral25-34 years oldManNoBisexualMiddle EasternNone of the aboveNone of the aboveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasyNaN
4344I code primarily as a hobbyEmployed, full-time;Student, part-timeFull in-personHobbySome college/university study without earning a degreeBooks / Physical media;Other online resources (e.g., videos, blogs, forum)Technical documentation;Written Tutorials;Stack OverflowNaN15.0NaNNaNNaNNaNOther (please specify):;Visit developer communities like Stack OverflowSwedenNaNNaNNaNNaNNaNNaNNaNNaNNaNNode.jsNaNNaNNaNnpmNaNNano;Notepad++;Vim;Visual Studio CodeVim;Visual Studio CodeLinux-basedLinux-basedGitCommand-lineNaNNaNNaNNaNSlackSlackFavorableStack OverflowA few times per month or weeklyYesI have never participated in Q&A on Stack OverflowNo, not at all35-44 years oldManNoBisexual;Straight / HeterosexualEuropeanNone of the aboveI have a mood or emotional disorder (e.g., depression, bipolar disorder, etc.);I have an anxiety disorder;I have a concentration and/or memory disorder (e.g., ADHD, etc.)NoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthNeither easy nor difficultNaN
4445I am a developer by professionStudent, full-timeNaNNaNSecondary school (e.g. American high school, German Realschule or Gymnasium, etc.)School (i.e., University, College, etc)NaNNaN6.0NaNNaNNaNNaNStart a free trial;Visit developer communities like Stack OverflowAustriaNaNNaNNaNBash/Shell;HTML/CSS;Java;JavaScript;Kotlin;Python;R;SQLBash/Shell;Java;Kotlin;SQLMySQLMySQLNaNNaNASP.NET;ASP.NET CoreNaN.NET;NumPy;Spring;Torch/PyTorchNumPy;Spring;TensorFlow;Torch/PyTorchDockerDockerEclipse;IntelliJ;PyCharmEclipse;IntelliJ;PyCharmWindowsLinux-based;Windows;Windows Subsystem for Linux (WSL)GitCommand-lineNaNNaNNaNNaNMicrosoft Teams;ZoomNaNUnsureCollectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack ExchangeMultiple times per dayYesA few times per weekYes, definitely18-24 years oldManNoStraight / HeterosexualWhite;EuropeanNone of the aboveNone of the aboveNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNAppropriate in lengthEasyNaN
4546I am a developer by professionEmployed, full-timeHybrid (some remote, some in-person)Other (please specify):Master’s degree (M.A., M.S., M.Eng., MBA, etc.)Other online resources (e.g., videos, blogs, forum);Online Courses or CertificationTechnical documentation;Video-based Online Courses;How-to videosUdemy4.03.0Developer, front-end500 to 999 employeesI have little or no influenceStart a free trialMadagascarEUR European Euro400.0MonthlyJavaScript;TypeScriptDartMongoDB;MySQLMongoDB;MySQL;PostgreSQL;Firebase Realtime DatabaseNaNNaNNode.js;React.js;Vue.jsNode.js;React.js;Vue.jsReact NativeFlutternpm;YarnDocker;npm;YarnVisual Studio CodeVisual Studio CodeNaNWindowsGitCommand-lineNaNNaNNaNNaNNaNNaNFavorableStack OverflowA few times per month or weeklyYesI have never participated in Q&A on Stack OverflowNo, not really25-34 years oldManNoStraight / HeterosexualAfricanNone of the aboveNone of the aboveYesIndependent contributor3.0Strongly agreeAgreeAgreeStrongly agreeStrongly agreeStrongly agreeAgree1-2 times a week1-2 times a weekNeverLess than 15 minutes a day15-30 minutes a dayVery longMicroservicesNoYesYesToo longEasy5124.0
4647I code primarily as a hobbyStudent, full-timeNaNNaNSecondary school (e.g. American high school, German Realschule or Gymnasium, etc.)Other online resources (e.g., videos, blogs, forum);Other (please specify):Technical documentation;Blogs;How-to videosNaN7.0NaNNaNNaNNaNOther (please specify):NorwayNaNNaNNaNBash/Shell;C;C++;Java;Lua;PythonBash/Shell;C++;Lua;PythonMongoDB;SQLiteSQLiteNaNNaNNode.js;React.jsNaNNaNNaNnpmNaNEclipse;IntelliJ;VimVimLinux-basedLinux-basedGitCommand-lineNaNNaNNaNNaNNaNNaNNaNCollectives on Stack Overflow;Stack Overflow for Teams (private knowledge sharing & collaboration platform for companies);Stack Overflow;Stack ExchangeMultiple times per dayYesMultiple times per dayYes, definitely18-24 years oldWomanYesPrefer to self-describe:White;EuropeanNone of the aboveI have autism / an autism spectrum disorder (e.g. Asperger's, etc.)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNToo shortNeither easy nor difficultNaN
4748I am a developer by professionEmployed, full-timeFully remoteHobbyBachelor’s degree (B.A., B.S., B.Eng., etc.)Friend or family member;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)Technical documentation;Blogs;Written Tutorials;Stack Overflow;Online books;Video-based Online Courses;Online challenges (e.g., daily or weekly coding challenges);Online forum;How-to videos;Written-based Online Courses;Interactive tutorialNaN6.05.0Developer, full-stack;Developer, back-end1,000 to 4,999 employeesI have little or no influenceStart a free trial;Visit developer communities like Stack Overflow;Ask developers I know/work withUnited States of AmericaUSD\tUnited States dollar135000.0YearlyJava;JavaScript;PHP;SQL;TypeScriptC++;GoCassandra;MySQLNaNNaNNaNAngular;Angular.js;jQueryNaNNaNNaNDocker;Homebrew;Kubernetes;npm;YarnNaNCLion;IntelliJ;PhpStormNaNmacOSmacOSGitCommand-lineNaNNaNConfluence;Jira Work ManagementNaNSlack;ZoomNaNIndifferentStack Overflow;Stack ExchangeDaily or almost dailyNoNaNNo, not really25-34 years oldWomanNoStraight / HeterosexualWhite;European;Asian;East AsianNone of the aboveI have an anxiety disorderYesIndependent contributor5.0Strongly agreeAgreeDisagreeDisagreeAgreeAgreeAgree1-2 times a week6-10 times a week1-2 times a week30-60 minutes a day30-60 minutes a daySomewhat longDevOps function;Microservices;Developer portal or other central places to find tools/services;Automated testing;Observability toolsYesYesYesAppropriate in lengthEasy135000.0
4849I am a developer by professionEmployed, full-timeHybrid (some remote, some in-person)Hobby;Contribute to open-source projectsSome college/university study without earning a degreeBooks / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)Technical documentation;Blogs;Written Tutorials;Stack OverflowNaN40.025.0Developer, back-end;Developer, desktop or enterprise applications20 to 99 employeesI have some influenceStart a free trial;Visit developer communities like Stack OverflowGermanyEUR European Euro100000.0YearlyHaskell;Java;RustDart;Haskell;Java;SwiftPostgreSQLNaNNaNNaNNaNNaN.NETFlutterNaNNaNEmacs;IntelliJ;Notepad++;Vim;Visual StudioAndroid Studio;Emacs;IntelliJ;Notepad++;Vim;Visual Studio;XcodeWindows;Windows Subsystem for Linux (WSL)Linux-based;macOSGitCode editor;Command-line;Version control hosting service web GUINaNNaNNaNNaNMicrosoft TeamsMicrosoft TeamsVery unfavorableStack Overflow;Stack ExchangeDaily or almost dailyYesA few times per month or weeklyYes, somewhat55-64 years oldManNoStraight / HeterosexualEuropeanNone of the aboveNone of the aboveYesNaN28.0Strongly agreeDisagreeNeither agree nor disagreeDisagreeDisagreeAgreeDisagree1-2 times a week3-5 times a weekNever15-30 minutes a dayLess than 15 minutes a dayJust rightContinuous integration (CI) and (more often) continuous delivery;Automated testingYesNoYesAppropriate in lengthEasy106644.0
4950I am a developer by professionEmployed, full-timeFully remoteHobbySecondary school (e.g. American high school, German Realschule or Gymnasium, etc.)Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc)Technical documentation;Blogs;Stack OverflowNaN7.04.0Developer, front-end;Developer, full-stack;Developer, back-end;DevOps specialist;Cloud infrastructure engineer100 to 499 employeesI have some influenceVisit developer communities like Stack OverflowGermanyEUR European Euro4000.0MonthlyC#;HTML/CSS;JavaScript;SQL;TypeScriptC#;HTML/CSS;JavaScript;SQL;TypeScriptPostgreSQLElasticsearch;PostgreSQLNaNNaNAngular;ASP.NET CoreAngular;ASP.NET CoreNaNNaNAnsible;Docker;npmAnsible;Docker;npmNano;Rider;Visual Studio Code;WebstormRider;Vim;Visual Studio Code;WebstormLinux-basedLinux-based;WindowsGitCode editor;Command-line;Version control hosting service web GUINaNNaNJira Work ManagementNaNMicrosoft TeamsNaNIndifferentCollectives on Stack Overflow;Stack Overflow;Stack ExchangeMultiple times per dayYesI have never participated in Q&A on Stack OverflowNeutral25-34 years oldManNoBisexualWhite;EuropeanNone of the aboveNaNYesIndependent contributor7.0Neither agree nor disagreeAgreeAgreeAgreeStrongly agreeAgreeDisagreeNever1-2 times a week1-2 times a week30-60 minutes a day15-30 minutes a daySomewhat longContinuous integration (CI) and (more often) continuous delivery;Automated testing;Observability toolsNoNoYesAppropriate in lengthEasy51192.0